Real-Time Telematics Fault Identification via Concurrent Processing
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Solution Overview
Problem
Existing vehicular telemetry systems face delays in processing big telematics data, leading to inefficiencies in real-time network communication fault identification and location determination due to resource-consuming processing methods and delayed data receipt.
Innovation Solution
A method involving concurrent processes on mobile and remote devices to determine communication faults by setting expected and actual communication periods, utilizing positional data and vehicle status to identify faults in real-time, with the system comprising a telemetry hardware system, communications microprocessor, and a positional device for GPS and accelerometer data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If data is delayed and copied to a separate database for processing, then processing resources are preserved, but data availability time increases significantly
Solution Approach 1:
The system performs preliminary actions by establishing communication period expectations in advance and continuously monitoring actual communications. This allows fault detection to occur proactively before data delays impact fleet management decisions, rather than waiting for batch processing to complete.
Solution Approach 2:
The system maintains continuous monitoring of communication periods between mobile devices and the remote device, enabling real-time fault detection. This continuous action eliminates the interruption caused by batch processing cycles, ensuring data availability without sacrificing processing resource management.
2Productivity
If batch processing is used during nighttime, then processing load is reduced, but real-time fault identification capability is lost
Solution Approach 1:
The system dynamically adjusts monitoring based on communication period expectations. By comparing expected versus actual communication patterns in real-time, the system can identify faults immediately when they occur, regardless of the time of day. This dynamic approach maintains reliability while allowing processing efficiency to be optimized through the concurrent process architecture.
Solution Approach 2:
The system implements feedback by continuously comparing actual communication data against expected communication periods. When deviations are detected, the system immediately identifies potential faults and can trigger appropriate responses. This feedback mechanism ensures real-time fault identification capability while maintaining processing efficiency through the structured concurrent process design.
3Loss of information
If complex raw data is processed and decoded, then meaningful analytics are produced, but processing time exceeds 12 hours per day
Solution Approach 1:
The system extracts only the critical communication period information from the complex raw telematics data for fault identification purposes. By focusing on this specific parameter rather than processing all raw data, the system maintains data meaningfulness for fault detection while dramatically reducing processing time requirements.
Solution Approach 2:
The system performs partial processing by monitoring only the communication period aspects of the data stream that are necessary for fault identification. This selective approach extracts the essential information needed for meaningful analytics while avoiding the excessive processing time of complete data decoding, achieving a balance between information quality and processing efficiency.
Data Source
AI summary
Apparatus, device, methods and system relating to a vehicular telemetry environment for identifying in real time unpredictable network communication faults in network zones based upon pre-processed raw telematics big data logs that may include GPS data and an indication of vehicle status data, and supplemental data that may further include location data and network data.