Vehicular Telematics Fault Detection Using Last Known Position
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Solution Overview
Problem
Existing vehicular telemetry systems face delays in processing and analyzing big telematics data, leading to impaired real-time fleet management and ineffective identification of network communication faults due to delayed data receipt and lack of augmented data.
Innovation Solution
A real-time big telematics data network communication fault identification system that includes mobile and remote devices, with defined communication modes and fault determination states, utilizing positional devices to determine communication faults based on last known positions, and employing a method to detect vehicle status for active or inactive modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is delayed and copied to separate databases for processing, then processing can be performed when active devices are at minimum, but real-time analytics capability deteriorates and decision-making is delayed
Solution Approach 1:
The system segments data processing into multiple parallel database instances (e.g., database 104a, 104b, 104c) that can handle different data streams simultaneously. This allows the system to process large volumes of telematics data in parallel while maintaining real-time analytics capabilities through dedicated query interfaces that can access processed data without waiting for batch processing cycles.
Solution Approach 2:
The system performs preliminary data processing and validation at the edge devices (mobile devices 102) before data is transmitted to the database. This preliminary action includes filtering, aggregating, and pre-processing data locally, which reduces the burden on centralized databases and enables faster real-time analytics by having data ready in an analyzable format before it reaches the processing layer.
2Use of energy by stationary object
If processing takes more than 12 hours per day of data, then resource consumption is reduced during peak hours, but analytics information becomes at least 1.5 days old
Solution Approach 1:
The system implements continuous data processing through multiple parallel database instances that operate simultaneously rather than batching all processing during off-peak hours. Each database instance continuously receives, processes, and makes data available for analytics in real-time, ensuring that analytics information is always fresh while distributing resource consumption across multiple systems to avoid overwhelming any single processor during peak periods.
Solution Approach 2:
The system dynamically adjusts processing priorities and resource allocation based on real-time conditions. During high-activity periods, the system can prioritize real-time processing for critical data streams while allowing less time-sensitive data to be processed asynchronously. This dynamic approach ensures that energy and computational resources are optimized based on actual demand rather than following a fixed batch processing schedule.
3Device complexity
If data delays and lack of augmented data occur, then network communication fault identification is impaired, but system complexity is reduced
Solution Approach 1:
The system introduces intermediary components including multiple database instances that act as buffers and data preparation layers between data collection and analytics processing. These intermediaries pre-process, validate, and organize data in real-time, ensuring that augmented and complete data is available for fault identification without requiring complex post-processing or retrospective analysis. The intermediary databases maintain data quality and completeness that enables reliable fault detection.
Solution Approach 2:
The system performs preliminary data augmentation and validation at the database level before data reaches analytics processing. This includes enriching raw telematics data with additional context, validating data completeness, and preparing augmented datasets that are ready for immediate analysis. By performing these actions preliminarily, the system ensures reliable fault identification without adding complexity to the analytics processing layer itself.
Data Source
AI summary
Apparatus, device, methods and system relating to a vehicular telemetry environment for the for identifying in real time unpredictable network communication faults 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.