Abstract: The standardization of process text data is crucial for data integration and reuse in manufacturing. To address the issue of inconsistent and non-uniform descriptions of process text data within manufacturing enterprises,a combined method of unsupervised data matching and supervised learning data matching was proposed,which integrating the BM25 algorithm and the DSSM algorithm to achieve low-cost standardization of process text data. First,the process text data was obtained and preprocessed from the enterprise′s process data management system,and an enterprise data dictionary was constructed based on the actual situation of the enterprise. Next,the unsupervised BM25 algorithm was used to coarsely match small batches of process text data with the enterprise data dictionary at the text similarity level.Experts then verified the coarse matching results to generate a training dataset. Finally,the training dataset was used to support the training of the DSSM algorithm based on supervised learning to achieve fine matching of process text data at the semantic similarity level. Validation was conducted on the standardization task of process names in a home appliance manufacturing company,demonstrating the effectiveness of the proposed method. This method can significantly reduce the labor costs involved in the standardization of process text data in manufacturing enterprises while ensuring the accuracy of the standardization process to the greatest extent possible.

Key words: computer integrated manufacturing, manufacturing, process text data, standardization, text matching, deep learning