Cloud Vehicle Diagnostics via Segmented Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current vehicle management systems lack efficient methods for real-time diagnostics, remote monitoring, and personalized service recommendations, leading to suboptimal vehicle maintenance and user experience.
Innovation Solution
A system that integrates vehicle onboard computers with cloud processing, enabling wireless communication for data exchange, crowd-sourced diagnostics, and personalized alerts, allowing for remote monitoring and service management through a Vehicle Service Website (VSW) that facilitates electronic key transfer for service providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If vehicle diagnostics are processed locally in the vehicle, then response time is fast, but processing capability and diagnostic accuracy are limited
Solution Approach 1:
The diagnostic processing is segmented into two parts: real-time monitoring and data collection performed locally in the vehicle, and comprehensive analysis and interpretation performed remotely in the cloud. This allows the vehicle to maintain fast local response while the cloud provides enhanced diagnostic accuracy through access to extensive databases and computing resources.
Solution Approach 2:
A cloud-based server acts as an intermediary between the vehicle's onboard systems and the driver/service providers. The server receives diagnostic data from the vehicle, processes it using crowd-sourced information and manufacturer databases, and returns actionable insights, thereby extending the vehicle's diagnostic capability without adding local processing power.
2Loss of information
If cloud processing is used for vehicle diagnostics, then diagnostic accuracy and insights are improved, but communication requirements and system complexity increase
Solution Approach 1:
The cloud-based server provides multiple functions through a single system: diagnostic data processing, crowd-sourced information aggregation, manufacturer database access, alert generation, and service scheduling. This multi-functionality reduces overall system complexity by consolidating what would otherwise require separate systems in the vehicle.
3Loss of information
If real-time monitoring is implemented, then vehicle status awareness is improved, but energy consumption and processing load increase
Solution Approach 1:
The monitoring system is segmented into lightweight local agents that collect and pre-process data, and a remote cloud server that performs intensive analysis. This division allows continuous monitoring with minimal local energy consumption, as the heavy processing occurs remotely.
Solution Approach 2:
Instead of continuous real-time processing, the system uses periodic data transmission and event-triggered communication. The vehicle monitors continuously but only communicates with the cloud when significant events occur or at scheduled intervals, reducing energy consumption while maintaining effective awareness.
4Ease of operation
If personalized service recommendations are provided, then user experience is improved, but data processing requirements and privacy concerns increase
Solution Approach 1:
The system provides personalized recommendations by processing user-specific data locally in the user's account profile while keeping the actual user data stored securely in encrypted form. The cloud server handles personalized processing without requiring exposure of sensitive user information, thus improving user experience while managing data privacy.
Data Source
AI summary
Methods, systems and computer readable media are provided. One example method includes establishing a connection with a vehicle over a wireless network, and associating the vehicle to a user account of an online service, wherein a vehicle type for the vehicle is identified in the user account. The method further includes receiving vehicle data for vehicle status information. The vehicle status information is for one or more vehicle systems of the vehicle. The method includes accessing one or more databases that include diagnostics data for the vehicle type and crowd sourced data for the vehicle type. The method includes processing the vehicle data that is received against the diagnostics data and the crowed sourced data. The processing is configured to select an alert from among a plurality of possible alerts. The crowd sourced data is configured to influence a confidence level for selecting the alert. The method includes sending a notification of the alert to the user account. The notification includes a recommended solution for handling the alert.