Intelligent testing method for railway CTC interface data based on fuzzy natural language processing
Fuzzy natural language processing applies fuzzy theoretical knowledge to the task of natural language processing (NLP). With the continuous development of large language model and artificial intelligence, research on text data continues to deepen. As a large and complex system, the interface data be...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
POSTS&TELECOM PRESS Co., LTD
2024-06-01
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| Series: | 智能科学与技术学报 |
| Subjects: | |
| Online Access: | http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-6652.202419 |
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| Summary: | Fuzzy natural language processing applies fuzzy theoretical knowledge to the task of natural language processing (NLP). With the continuous development of large language model and artificial intelligence, research on text data continues to deepen. As a large and complex system, the interface data between various subsystems and server software are stored and transmitted in log text format. Due to its large number of texts and miscellaneous text types, a fuzzy NLP method was proposed to solve the problem of manual testing the interface data of centralized traffic control (CTC) system. The fuzzy C-means (FCM) clustering algorithm divided the log text into different label categories, which was used as the label input for named entity recognition in NLP tasks, and BERT was introduced on the traditional BiLSTM-CRF model for text encoding, which understood the relationship between texts more accurately and improved the accuracy of text recognition. An intelligent verification tool for log-text interface testing of railway CTC system was presented based on an improved training model, which enhanced the current manual testing process of CTC system, assisted testing staff in verifying the interface testing, and increased the level of intelligence and automation in testing work. |
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| ISSN: | 2096-6652 |