Research on the Construction of a Database for the Main Research Contents of Vehicle Evaluation Based on Multi-Source Data

This research focuses on constructing a comprehensive repository for vehicle assessment studies, leveraging diverse data sources to enhance the precision and thoroughness of vehicle evaluations. The research introduced a multi-source data amalgamation algorithm capable of efficiently merging various data streams, including sensor readings, user input, and past operational records. This algorithm employs techniques such as the weighted average method, fuzzy logic, and machine learning to preserve the inherent attributes of the data during the fusion process and to refine the accuracy of the evaluation outcomes. To validate the algorithm's effectiveness, extensive experiments were conducted in a simulated environment, where the algorithm's performance was evaluated across varying data dimensions and levels of noise. The results from the simulations indicate that the algorithm excels in monitoring vehicle conditions and assessing performance, achieving an error margin below 5%, and demonstrating robust capability in handling noisy data, thereby ensuring the dependability of the evaluation results. Furthermore, people established a structured database to archive and manage the processed data, facilitating rapid retrieval and analysis. The database is intricately connected with the algorithm, guaranteeing the system's ability to efficiently manage large-scale data and deliver precise decision-making support for vehicle assessments.