Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (4): 174-181.doi: 10.11896/jsjkx.230400031

• Database & Big Data & Data Science • Previous Articles     Next Articles

Study on Manufacturing Company Automated Chart Analysis Method Based on Natural LanguageGeneration

WANG Xu1, LIU Changhong2, LI Shengchun2, LIU Shuang3, ZHAO Kangting1, CHEN Liang1   

  1. 1 School of Computer Science,Xi'an Polytechnic University,Xi'an 710048,China
    2 China Tobacco Chongqing Industrial Co.Ltd.,Qianjiang Cigarette Factory,Chongqing 409000,China
    3 School of Mathematics and Statistics,Shaanxi Normal University,Xi'an 710119,China
  • Received:2023-04-05 Revised:2023-06-20 Online:2024-04-15 Published:2024-04-10
  • Supported by:
    Key Scientific Research Program of the Education Department of Shaanxi Province,China(22JS021).

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Abstract: With the wave of digital transformation,manufacturing enterprises produce a large number of chart data every day.Traditional chart analysis methods are difficult to analyze chart data efficiently and accurately.Automated chart analysis methods have become an important means of chart analysis.In order to solve the problem that the automatic chart analysis method is difficult to meet the specific needs in practical application,an automatic chart analysis method of manufacturing enterprises based on natural language generation is proposed.This method analyzes the chart data based on LSTM,and in order to solve the problem of misleading LSTM by redundant data in the analysis process,a discriminator layer is added after the embedding layer to enable LSTM to perform more targeted semantic understanding and text prediction according to the type of chart.Aiming at the problem of poor quality of description sentences generated in the process of diagram analysis,a random cluster sampling strategy is proposed to improve the quality of diagram analysis by referring to beam search and random sampling strategy,and knowledge distillation method is introduced to optimize LSTM to further improve the quality of description text.Experiments show that this method improves the text quality by 8.9% compared with LSTM.In order to apply the method in practice,an automatic chart analysis system for manufacturing enterprises is designed and developed,and the method is introduced as a chart analysis tool.Experimental results show that the application of this method can improve the quality and efficiency of chart analysis in manufacturing enterprises.

Key words: Chart analysis, Natural language generation, LSTM, Knowledge distillation

CLC Number: 

  • TP391.41
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