Research And Application Of Steel Quality Defect Traceability Technology Based On Data Mining And Knowledge Graph

With the rapid development of the steel industry,improving the quality of steel products and reducing quality defects has become a key task for steel companies.The application of artificial intelligence technology in the steel industry can realize intelligent quality control and improvement.Based on the method of data mining and knowledge graph,this paper studies and applies an innovative steel quality defect traceability technology.This technology aims to reveal the potential influencing factors leading to quality defects by analyzing a large amount of data,and provide reference and guidance for steel quality improvement.First of all,in this study,the data mining technology is used to analyze the steel production data,reveal the internal relationship between various parameters in the production process,and summarize the causes of quality defects in steel products.The method of data preprocessing is used to analyze the attribute correlation and solve the problem of data imbalance,and the decision tree model is employed for analyzing the importance of different characteristic parameters on the occurrence of steel defects and summarize the rules of the cause of steel defects.Secondly,a knowledge graph of steel defect causes is constructed,and the knowledge graph technology is used to provide a structured representation.Based on the Neo4j graph database,the knowledge mined through data mining technology is used for knowledge modeling,and a structured knowledge network is established,which enables scattered information to be integrated to form a more complete and comprehensive steel quality knowledge system,and provide decision support functions for quality traceability.Finally,quality traceability research is conducted based on the knowledge graph of steel defect causes.The quality traceability model has been developed based on the path ranking algorithm in graph computing technology.This model enables the identification of relevant event paths that contribute to quality defects,quantitatively analyzes the attribution probability of each event path,and provides visual representations of the results.By recommending the quality event path with the highest probability of causing defects,the traceability of steel quality defects is realized.In addition,compared with the traditional method of using the bayesian network to complete the traceability of steel defects,the advantages of the steel defect traceability model based on data mining and knowledge graphs are analyzed.Through the above work,a steel quality defect traceability technology is proposed based on data mining and knowledge graph,which can improve the accuracy and efficiency of quality traceability,help steel companies discover potential problems in steel products,and further promote the quality of steel products.The development of quality control and quality improvement promotes the sustainable development of the iron and steel industry,and also provides reference and inspiration for quality traceability research in other industries.