Asset Tracking Data Capture with Dual Simplification and Rich Logging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Telematics systems face challenges in efficiently processing large volumes of data from asset tracking devices, leading to overwhelming technical infrastructure and a lack of rich detail for advanced analytics, as existing methods either discard valuable data or burden the system with excessive information.
Innovation Solution
Implementing a dual data capture approach where asset tracking devices apply a dataset simplification algorithm in parallel with a rich data capture algorithm, selectively transmitting simplified data for telematics services while logging and forwarding unsimplified blocks for rich data analysis when specific triggers are met, allowing for machine learning analysis and detailed event capture without overloading the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all raw data is transmitted to the server for processing, then rich data availability for advanced analytics is improved, but system infrastructure burden increases and processing efficiency decreases
Solution Approach 1:
The patent segments data processing into two parallel paths: a simplified data path for telematics services and a rich data path for advanced analytics. The asset tracking device divides raw data into simplified datasets (for routine telematics) and unsimplified blocks (for detailed analysis), allowing each path to handle different data volumes and requirements independently, thus resolving the contradiction between data richness and processing efficiency
Solution Approach 2:
The patent extracts only the necessary simplified data for routine telematics services while preserving unsimplified blocks for advanced analytics. By taking out only the essential simplified data for general processing and leaving detailed unsimplified blocks available when needed, the system reduces infrastructure burden while maintaining rich data availability for specific analytical purposes
2Productivity
If data simplification is applied to reduce processing load, then system infrastructure burden is reduced, but data detail and richness for advanced analytics is lost
Solution Approach 1:
The patent segments the data processing workflow into parallel streams: one that applies simplification algorithms to reduce data volume for routine telematics services, and another that preserves full data detail for advanced analytics. This segmentation allows the system to achieve processing efficiency through simplification while maintaining data richness in the unsimplified path for analytical purposes
Solution Approach 2:
The patent applies local quality by differentiating data handling based on purpose: simplified data quality is sufficient for routine telematics operations, while full unsimplified data quality is maintained for advanced analytics. This localized differentiation of data quality levels allows the system to optimize processing efficiency where appropriate while preserving data richness where needed
3Reliability
If continuous monitoring of raw data is performed, then data capture completeness is improved, but system complexity and processing overhead increase
Solution Approach 1:
The patent performs preliminary action by pre-configuring trigger conditions and data capture rules at the asset tracking device before data collection begins. The device monitors raw data continuously but only initiates detailed capture and transmission when predefined triggers are met, reducing system complexity during normal operation while maintaining completeness when events occur
Solution Approach 2:
The patent implements periodic action through trigger-based data capture mechanisms that activate at specific moments (when triggers are satisfied) rather than continuously processing all data. This periodic activation of detailed monitoring and transmission reduces system complexity during idle periods while ensuring data capture completeness when relevant events occur
Data Source
AI summary
Methods, systems, and devices for data capture instructions for asset tracking are provided. An example method for capturing data involves obtaining raw data from a data source onboard an asset and monitoring the raw data for satisfaction of a simplified data capture trigger. When the simplified data capture trigger is satisfied, a dataset simplification algorithm is performed on the raw data to generate a simplified set of raw data, and the simplified set of raw data is logged. The method further involves monitoring the raw data for satisfaction of a rich data capture trigger. When the rich data capture trigger is satisfied, an unsimplified block of raw data is identified and logged for rich data analysis. The data is transmitted to a server. The unsimplified block of raw data contains raw data that is additional to the raw data contained in the simplified set of raw data.