Extrema-Retentive Data Buffering for Asset Tracking
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Solution Overview
Problem
Telematics systems often inadvertently overlook extrema data points, such as peaks or troughs, during the data simplification process, especially when transitioning from one data buffer cycle to another, leading to incomplete data recording.
Innovation Solution
An extrema-retentive data buffering and simplification process is implemented, where a dynamic group of carry-over data points, including the last recorded point and points deviating significantly from reference lines, are retained across cycles to ensure accurate capture of extrema data points, using a dataset simplification algorithm that compares data points against multiple threshold values to determine recordation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data simplification is performed on complete cycles of raw data, then data processing efficiency is improved, but extrema data points may be overlooked or lost during cycle transitions
Solution Approach 1:
The patent applies preliminary action by pre-processing the raw data stream to identify and flag potential extrema data points before the simplification algorithm processes the data. This allows the system to maintain processing efficiency while ensuring extrema points are not overlooked during cycle transitions, as they are marked in advance for special handling.
Solution Approach 2:
The patent introduces an intermediary mechanism that bridges the gap between complete cycle processing and extrema point preservation. This intermediary layer analyzes transition points between cycles and ensures that extrema data points are carried over or specially handled, allowing the system to maintain both processing efficiency and data accuracy.
2Loss of information
If all raw data points are recorded, then complete data representation is achieved, but data storage requirements and system resource consumption increase
Solution Approach 1:
The patent extracts only the most significant data points (extrema points) from the complete raw data stream for permanent recording, while allowing other redundant points to be discarded. This extraction approach maintains complete data representation for critical events while dramatically reducing storage requirements by filtering out unnecessary intermediate data points.
Solution Approach 2:
The patent applies local quality by differentiating the treatment of data points based on their local characteristics. Extrema points that represent critical events receive special preservation treatment, while ordinary data points are subject to compression or elimination. This selective approach ensures important information is retained while reducing overall storage demands.
3Device complexity
If data buffer size is reduced to conserve memory resources, then device complexity and resource consumption are reduced, but the ability to capture extrema points during simplification deteriorates
Solution Approach 1:
The patent uses preliminary action to identify and mark extrema data points before buffer overflow occurs. By pre-processing the data stream to flag significant points, the system can preserve these marked points even in small buffers, maintaining reliability without requiring large memory resources.
Solution Approach 2:
The patent introduces an intermediary mechanism that monitors buffer status and actively protects extrema points from being overwritten. This intermediary layer ensures that even when the buffer is full, critical extrema data points are preserved by making special handling decisions, allowing small buffers to maintain high capture accuracy.
Data Source
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AI summary
Methods and devices for asset tracking are provided. An example method involves obtaining a stream of raw data, adding data points from the stream of raw data to a data buffer in a first cycle of data, performing a dataset simplification algorithm on the first cycle of data to determine whether one or more data points from the first cycle of data are to be recorded, preparing the data buffer for a second cycle of data, including determining a group of carry-over data points to be included in the second cycle of data, and continuing to add data points from the stream of raw data to the data buffer in the second cycle of data.