Vehicle Predictive Control System Using Big Data Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current vehicle technologies lack the ability to predict and prevent issues like engine stall, fuel efficiency reduction, and power performance degradation using Artificial Intelligence and big data, posing safety risks due to the lack of predictive control mechanisms.
Innovation Solution
A vehicle predictive control system that uses big data and AI to collect and analyze in-vehicle device status information, classifying problem occurrence conditions and providing learned information to controllers for feedforward control, thereby preventing issues like engine stall and fuel efficiency reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicle status information is collected and analyzed using AI and big data, then predictive control capability is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system divides the predictive control function into multiple components: vehicle terminals for data collection, big data service providers for analysis, and controllers for execution. This segmentation allows each component to specialize in specific tasks, improving overall reliability while managing complexity through modular architecture.
Solution Approach 2:
A big data service provider acts as an intermediary between vehicle terminals and controllers, processing complex AI analysis and transforming raw status information into actionable predictive control commands. This intermediary handles the computational complexity centrally, allowing vehicle terminals to remain relatively simple while achieving advanced predictive capabilities.
2Measurement precision
If real-time status information is collected from multiple vehicles, then prediction accuracy is improved, but data transmission and processing load increase
Solution Approach 1:
The system extracts only relevant status information from vehicles for transmission to the big data service provider, rather than transmitting all raw data. This selective extraction maintains prediction accuracy by focusing on critical parameters while significantly reducing data transmission load and energy consumption.
Solution Approach 2:
Data from multiple vehicles is merged and aggregated at the big data service provider, allowing the system to leverage collective information for improved prediction accuracy. This centralized merging approach enables comprehensive analysis without requiring each individual vehicle to process and transmit excessive data.
3Reliability
If predictive control is implemented to prevent engine stall and failure, then vehicle safety is improved, but control intervention complexity increases
Solution Approach 1:
The system performs preliminary analysis of status information to predict potential engine failures before they occur. By identifying risk patterns in advance using AI and big data, the system can prepare appropriate control interventions, improving vehicle safety while managing complexity through proactive rather than reactive control strategies.
Solution Approach 2:
The system implements a feedback loop where predicted risk information is continuously communicated from the big data service provider to vehicle controllers, which then execute appropriate control actions. This feedback mechanism enables systematic management of control intervention complexity by providing structured guidance based on predictive analysis results.
Data Source
AI summary
A vehicle predictive control system based on big data includes: a vehicle terminal, which is installed in each of a plurality of vehicles, collecting status information related with an in-vehicle device in a corresponding vehicle to transmit the collected status information in real time, and transmitting problem occurrence information upon problem occurrence of the in-vehicle device; and a big data service provider classifying and storing the status information received from the vehicle terminal as big data, and obtaining a problem occurrence condition based on the status information to transmit information corresponding to the problem occurrence condition to the vehicle terminal when receiving the problem occurrence information of the in-vehicle device from the vehicle terminal of at least some vehicles among the plurality of vehicles.