Vehicle Parameter Reporting Configurations from Historical Fault Analysis
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Solution Overview
Problem
Existing vehicle parameter reporting configurations are inefficient and require extensive manual customization by engineers, lacking intelligent guidance and failing to adapt to changing policies, regional variations, and operational environments, leading to sub-optimal data reporting that consumes resources and hinders effective troubleshooting and maintenance.
Innovation Solution
A computer-implemented method and system that generates vehicle operation-related scenarios by calculating parameter variance and fault occurrence probability, using historical data and machine learning, to recommend optimized reporting configurations, including parameters and criteria, which can be updated and integrated with policy and regional attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual customization of vehicle parameter reporting configurations is performed, then configuration accuracy can be improved, but engineering time and resource consumption increase significantly
Solution Approach 1:
The system performs self-service by automatically generating vehicle parameter reporting configurations based on historical data and machine learning models, eliminating the need for manual engineering customization while maintaining high accuracy through algorithmic optimization
Solution Approach 2:
The system changes parameters by using machine learning models to automatically determine optimal parameter values for reporting configurations, transforming manual parameter selection into automated computational optimization based on historical patterns
2Reliability
If comprehensive data reporting is implemented, then troubleshooting and maintenance effectiveness improve, but resource consumption and system complexity increase
Solution Approach 1:
The system extracts only the necessary data by using machine learning models to identify and select relevant vehicle parameters for reporting, filtering out unnecessary data while maintaining troubleshooting effectiveness through intelligent data selection
Solution Approach 2:
The system implements feedback mechanisms where historical data and outcomes are fed back into the machine learning model to continuously optimize reporting configurations, improving troubleshooting effectiveness while adapting to changing operational patterns
3Ease of manufacture
If reporting configurations are manually configured, then initial setup can be completed, but adaptability to changing policies and regional variations is insufficient
Solution Approach 1:
The system transitions from static manual configuration to dynamic automated configuration that continuously adapts to changing policies and regional variations through machine learning models that learn from updated historical data and operational patterns
Solution Approach 2:
The system performs preliminary action by pre-configuring reporting parameters based on historical data and policy patterns before actual operation, enabling rapid adaptation to new policies through pre-learned patterns and reducing manual reconfiguration needs
Data Source
AI summary
A vehicle parameter reporting configuration generation system models historical data, calculates vehicle parameter variance and/or fault occurrence probability, and generates vehicle operation-related scenarios accordingly. Policy documents and region-based attributes may be incorporated into the vehicle operation-related scenarios. The vehicle operation-related scenarios are utilized to generate recommended vehicle parameter reporting configurations, including recommended vehicle parameters to be reported, and reporting criterion such as relating to timing, frequency, and/or retention. The recommended vehicle parameter reporting configurations may be reviewed and/or modified via a user interface and loaded onto an onboard system to direct the onboard system or subsystems thereof to report certain vehicle parameter and reporting criterion, such as during operation of a vehicle.