Source-Aware Data Compression for Long-Term Industrial Control Archives
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Solution Overview
Problem
Current data storage strategies for industrial automation control systems, power distribution systems, and power generation systems are inadequate for detailed long-term analysis, leading to loss of valuable information due to coarse data granularity and excessive storage requirements, which hinders the ability to analyze patterns and optimize system performance.
Innovation Solution
A machine learning approach is employed to compress data from various sources, automatically selecting and optimizing compression techniques and parameters to reduce storage needs, allowing for efficient long-term data retention and analysis, while minimizing communication bandwidth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is stored with fine granular detail for long-term analysis, then analysis quality and pattern detection capability are improved, but storage space requirements increase excessively
Solution Approach 1:
The patent segments data into different granularity levels based on time and importance. Fine-granular data is retained for recent periods while coarser aggregation is applied to historical data, allowing detailed analysis when needed while reducing overall storage requirements for long-term archives.
Solution Approach 2:
The patent changes the parameter of data granularity over time. Recent data is stored at fine granularity for detailed analysis, while historical data is stored at coarser granularity levels, optimizing the balance between analysis capability and storage efficiency across different time periods.
2Quantity of substance
If data is aggregated at coarse granularity for long-term storage, then storage space requirements are reduced, but analysis capability and pattern detection are degraded
Solution Approach 1:
The patent divides stored data into multiple granularity levels or tiers. This segmentation allows the system to provide both fine-granular data for detailed analysis and coarser aggregated data for long-term trends, satisfying both storage efficiency and analysis capability requirements simultaneously.
3Duration of action of stationary object
If detailed data is stored for longer durations to cover weekly, monthly, seasonal, and yearly patterns, then long-term analysis capability is improved, but storage requirements become excessive
Solution Approach 1:
The patent implements a multi-level data retention strategy where data is segmented by time period and granularity. Recent data retains fine granularity for detailed pattern detection, while historical data progresses through coarser aggregation levels, enabling long-term storage of yearly patterns without excessive storage requirements.
Solution Approach 2:
The patent applies time-varying granularity parameters to data storage. As data ages, the granularity parameter automatically changes from fine to coarse, allowing the system to retain detailed information for short-term analysis while maintaining compressed representations for long-term historical analysis.
4Measurement precision
If fine granular data is retained for detailed analysis, then pattern detection and optimization capability are improved, but communication bandwidth requirements increase
Solution Approach 1:
The patent dynamically changes the data granularity parameter based on the analysis needs and time horizon. For detailed short-term pattern detection, fine-granular data is transmitted and stored. For long-term trend analysis, coarser aggregated data suffices, reducing communication bandwidth requirements while maintaining adequate analysis capability.
Data Source
AI summary
To facilitate storage of data from plural data sources of an industrial automation control system, power distribution system or power generation system, a decision making device executes a machine learning algorithm to determine a compression technique in dependence on the data source from which data originates.