GPT驱动的新能源车故障诊断与解决方案生成研究

Research on GPT Driven New Energy Vehicle Fault Diagnosis and Solution Generation

随着我国新能源汽车普及率的逐年提高,其故障率也呈上升趋势,为了确保新能源汽车的安全可靠运行,文章旨在探索一种故障信息快速准确诊断和解决的方法。采用基于生成式预训练Transformer模型(GPT)的自然语言处理方法,通过充分利用其强大的语言模型,实现了从故障信息到解决方案的端到端自动化处理流程。实验过程中,构建了两个丰富的语料库,分别从用户自然语言描述和科技论文中提取相关信息,以充分支持模型的训练和测试需求。基于GPT的预训练模型,对模型进行了微调操作,以提升其适应性和性能水平。为了客观全面地评估模型的生成效果,采用BLEU和ROUGE两种评估指标。同时,对生成的解决方案进行了主观和详细地分析,以展示模型的优势和实际效果。根据实验结果分析,所提出的模型在生成解决方案时表现出更高水平的语义一致性、完整性和准确性,相较于对比模型具有显著优势。

With the increasing popularity rate of new energy vehicles in China year by year,the failure rate is also on the rise.In order to ensure the safe and reliable operation of new energy vehicles,this study aims to explore a method of rapid and accurate diagnosis and solution provision based on fault information.In this study,a natural language processing method based on the generative pre-trained transformer model(GPT)is adopted to realize the end-to-end automated processing process from fault information to solution by making full use of its powerful language model.In the experimental process of this study,we constructed two rich corpora to extract relevant information from users' natural language descriptions and scientific papers respectively to fully support the training and testing requirements of the model. Based on the pre-trained model of GPT, this study fine-tuned the model to improve its adaptability and performance. In order to evaluate the generation effect of the model objectively and comprehensively, two evaluation indexes BLEU and ROUGE are used in this study. At the same time, we also conduct a subjective and detailed analysis of the generated solution to demonstrate the advantages and practical effectiveness of the model. According to the analysis of experimental results, the model proposed in this study shows a higher level of semantic consistency, completeness and accuracy when generating solutions, which has significant advantages over the comparison model.