AIOT Based Vehicle Maintenance System

Abstract

The AIoT-Based Vehicle Maintenance System aims at enhancing the management of vehicles maintenance through the integration of smart tech using the focus on the user experience. User data on all aspects of part maintenance is available by means of a personal dashboard getting easy access to the vehicle’s condition. The system does more than just monitor; Real time alerts, part comparisons and condition specific predictive updates are also included in this system’s offerings. The system maximizes both cost effectiveness and overall reliabilty of vehicles. The platform provides personalized notifications as to when maintenance needs to be executed by examining weekly distance and average speed. App users are informed of specific instances when replacement of these particular components is due, and based on the user’s driving patterns they are given pertinent repair advice. Conditions of the roads are also analyzed to determine when the tasks of road maintenance should be carried out accordingly. Furthermore, the platform also evaluates drivers’ driving behaviour, by for example recording instances of when there are problems in the system and when there aren’t, so as to aid estimation of the life cycle of vehicle components. The workshops can take advantage of a web portal that comes with AI-based spare part suggestions, real time inventory tracking of a nearby inventory and comparative pricing alternatives and are given authority to send maintenance alerts to users customized to their full maintenance logs. A device with AIoT technology is also part of the system, tracking the vehicle’s travels with IoT sensors and sending the data straight to the mobile app so maintenance predictions are accurate. Also, the built-in scraper gathers real-time price data from Ali Express, so users can decide which items are best for them. The application is based on a advanced and scalability-friendly tech platform. The mobile aspect is created with Flutter, which will guarantee a natural and unified user experience. React.js is used to build workshops’ web portal to provide quick and interactive experiences. The Flask APIs handle the backend and data is stored in real time with Firebase. Training models requires Python and TensorFlow.js and Scikit-learn are added to offer important prediction capabilities. Using Git and GitHub, developers can control the code and make it easier to develop using VS Code, encouraging effective coding and easy collaboration within the team.