Bayesian Signal Segmentation for Real-Time Compression Updates
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Solution Overview
Problem
Current data compression techniques in industries like hydrocarbons often require buffering large amounts of data before compression, which is inefficient, especially when dealing with real-time data and limited bandwidth or storage constraints, and do not effectively handle the increasing amount of detailed data from multiple sensors.
Innovation Solution
The method involves segmenting data into segments with defined boundary points and parameters, using maximum a posteriori analysis to select the most likely segmentation, and transmitting or storing only the segment boundary points and parameters, allowing for dynamic adjustment based on bandwidth constraints and updating segmentations in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If data is buffered in large amounts before compression, then compression can be performed as a block, but real-time transmission capability deteriorates and storage requirements increase
Solution Approach 1:
The patent divides the data stream into segments with defined boundary points and parameters, allowing compression to occur on segmented data rather than requiring large buffered blocks. This enables real-time processing while maintaining compression effectiveness through the use of segment boundary points that identify significant changes in the data signal.
Solution Approach 2:
The system dynamically adjusts segmentation parameters and boundary points based on the actual data characteristics as they arrive in real-time. The maximum a posteriori analysis continuously updates the most likely segmentation as new data samples are processed, enabling adaptive compression that responds to changing data patterns without requiring pre-buffering.
2Loss of information
If all sensor data is transmitted without compression, then data completeness is maintained, but bandwidth consumption increases
Solution Approach 1:
The patent extracts only the essential information from the sensor data stream by identifying and transmitting only the segment boundary points and segment parameters that define the signal characteristics. This extraction approach maintains the ability to reconstruct the original signal accurately while dramatically reducing the amount of data that needs to be transmitted over the bandwidth-constrained channel.
Solution Approach 2:
The system transforms the raw sensor data into a parameter-based representation where each segment is defined by boundary points and parameters (such as slope, intercept, or other characteristic values). This parameter transformation reduces data volume while preserving the essential information needed to represent the original signal, effectively managing the trade-off between data completeness and bandwidth usage.
3Measurement precision
If segmentation is performed with many boundary points, then signal accuracy is improved, but data transmission volume increases
Solution Approach 1:
The patent applies partial action by selectively identifying only those segment boundary points that are necessary to accurately represent the signal, rather than creating segments at every possible change point. The maximum a posteriori analysis determines the optimal number and location of boundary points, using just enough segmentation to capture signal characteristics without over-segmenting, thus balancing accuracy with data reduction.
Data Source
AI summary
Systems, methods, and devices are provided for compressing a variety of signals, such as measured signals from a hydrocarbon operation, that may be stored and/or transmitted in compressed form. Segmentation tools and techniques are used to compress the signals. Segmentation techniques include breaking a signal into segments and representing the data samples of the signal as segment boundary points, which may reflect where changes occur in the signal, and segment parameters, which may be utilized to model the segmented data. Embodiments can be used in real-time or in batch modes. New data samples can influence previous segment boundary points and/or segment parameters in some cases. Systems may modify what has already been stored or displayed as a result in revising segmentation information based on analysis utilizing the new data samples. Embodiments may utilize different Bayesian analysis techniques including the use of prior probability distributions and maximum a posteriori analyses.