# Large Language Model Based Intelligent Fault Information Retrieval System for New Energy Vehicles
## 4 Results
### Data Acquisition and Preprocessing  
The data sources mainly include text, audio, video, and other digital resources on the internet. In the study, we accessed various available resources, websites, and online databases related to automobiles through manual statistics and web crawler technology. After analyzing the structural features and data feedback methods of various websites, we systematically merged and integrated NEV data, which served as the basis for constructing the NEV knowledge graph and intelligent fault data retrieval system.  
The dataset pertaining to NEVs encompasses three primary categories: battery electric vehicles, plug-in hybrid electric vehicles, and range-extended electric vehicles. This comprehensive dataset comprises essential details regarding the specific specifications and technological types of existing NEVs. A cumulative total of 822 distinct vehicle models has been collated, which includes 631 battery electric vehicle models, 35 range-extended electric vehicle models, and 171 plug-in hybrid electric vehicle models. Moreover, an extensive compilation of 25,000 fault types has been compiled into 12 principal classifications, with further subdivision into 122 subcategories based on diverse fault characteristics. Additionally, an impressive repository of 22,709 instances of expert knowledge has been meticulously sorted out under various fault scenarios, providing a robust foundation for the preprocessing of data and the generation of knowledge for LLMs.  
In the application of LLMs, it is imperative to undergo both training and fine-tuning phases, particularly in the context of QA-type applications, wherein the objective is to facilitate effective question-answering. The process of fine-tuning is systematically executed in accordance with the following steps:  
Step 1: Data sanitization, which entails the meticulous purification of the original dataset by eliminating special characters, punctuation, HTML tags, etc., thereby guaranteeing the integrity and uniformity of the information.  
Step 2: Data format conversion is the transformation of the sanitized data into a JSON format that is conducive to model ingestion, typically structured in an interactive question-answer paradigm. Each data sample comprises a query and a corresponding response, delineated by specific delimiters. An example of the data format conversion is as follows: question, [\"conversations\": [[\"role\": \"user\", \"content\": \"Throttle Position Sensor, Structure and Working Principle.\"], \"answer\", [\"role\": \"assistant\", \"content\": \"Greetings! The throttle position is situated posterior to the hose and antecedent to the manifold. The throttle constitutes a regulatable valve that governs the inflow of air into the engine. Subsequent to the air's entry into the intake manifold, it is combined with gasoline to create a flammable mixture, which is thereafter ignited to generate power.\"]]].  
Step 3: Insertion of demarcation tokens, to apprise the model when it should start processing the problem and the corresponding answer. Special tokens, including [gMASK],, <! system! >, <! assistant! >, and others, are interspersed at the commencement, conclusion, and specific junctures within the input text, thereby facilitating the model's accurate comprehension of the input data.  
Step 4: Encoding of data, entailing the conversion of the annotated text into a recognizable code format for the model. This process employs word embedding techniques to transform textual data into vector representations and supplements them with positional encoding to constitute the model's input vector.  
Following these preprocessing procedures, the primal dataset is meticulously converted into an appropriate format conducive to the fine-tuning of LLMs, subsequently undergoing meticulous annotation and encoding processes [45,46]. This measure can significantly enhance the efficacy of LLMs when applied to the downstream task of expert knowledge acquisition for NEVs.