Vehicular Telematics Fault Detection With Real-Time Data Segmentation
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Solution Overview
Problem
Existing vehicular telemetry systems face delays in processing big telematics data, leading to inefficient fleet management decisions and impaired ability to detect network communication faults in real-time due to delayed data processing and lack of augmented data.
Innovation Solution
A real-time big telematics data network communication fault identification system that includes mobile devices and remote devices capable of monitoring expected and actual communication states, using positional data to determine fault locations, and adjusting communication modes based on vehicle status, with a system architecture that segregates and processes raw data into meaningful analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is delayed and copied to separate database for processing, then data processing capacity is improved, but real-time analytics capability deteriorates
Solution Approach 1:
The system segments data processing into multiple parallel pathways: real-time processing stream and batch processing stream. The real-time stream processes data immediately for timely analytics, while the batch stream handles historical data for comprehensive analysis, eliminating the need to choose between processing capacity and real-time capability.
Solution Approach 2:
The system performs preliminary data validation, filtering, and transformation at the data collection stage before data enters the processing pipeline. This preliminary action reduces the complexity and volume of data requiring full processing, enabling both high processing capacity and real-time analytics by preparing data in advance for multiple processing modes.
2Loss of information
If processing and decoding of big telematics raw data is performed, then meaningful analytics are produced, but processing time increases
Solution Approach 1:
The system applies partial processing to data streams based on priority and time sensitivity. Critical real-time parameters receive immediate processing and decoding, while less time-sensitive data is processed in batches. This selective partial action ensures meaningful analytics are produced for urgent decisions without incurring full processing time for all data.
Solution Approach 2:
The system implements continuous real-time processing of essential telematics parameters while simultaneously performing batch processing of complete data sets. This continuity ensures that meaningful analytics are continuously available for real-time decisions, while comprehensive processing occurs in parallel without interrupting the real-time flow.
3Reliability
If network communication fault detection is performed using delayed data, then fault identification is achieved, but fault location precision deteriorates
Solution Approach 1:
The system implements real-time feedback loops that continuously monitor communication status and compare expected versus actual data receipt. This immediate feedback enables precise fault identification and location by detecting deviations at the moment they occur, using current positional data from mobile devices to accurately pinpoint fault locations without the delays that would reduce precision.
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.