Sensor Data Prediction Filtering for Storage Reduction
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Solution Overview
Problem
Current data management systems face challenges in efficiently reducing the amount of data to be stored from multiple sensors, particularly when the data from one sensor is accurately predictable from another, leading to unnecessary data storage and potential storage capacity issues.
Innovation Solution
A data management system that acquires measurement data from multiple sensors, predicts data using measurement data from another sensor, and selectively deletes data from the target sensor based on differences and predetermined thresholds to reduce data storage, including reducing the number of samples or data size, while ensuring critical data is preserved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If measurement data from all sensors is stored, then data completeness is maintained, but storage capacity is excessively consumed
Solution Approach 1:
The patent extracts only the essential measurement data that cannot be predicted from other sensors, while removing redundant data that can be accurately reconstructed. This selective extraction maintains data completeness for critical information while eliminating unnecessary data storage.
Solution Approach 2:
The patent changes the parameter of data selection from storing all sensor data to storing only data based on prediction accuracy thresholds. By dynamically adjusting which data points are stored based on their predictability from other sensors, the system optimizes storage capacity while maintaining reliability.
2Reliability
If data from multiple sensors is stored, then data availability is ensured, but data processing complexity increases
Solution Approach 1:
The patent extracts and stores only the essential measurement data that cannot be predicted from other sensors. This reduction in stored data volume directly decreases data processing complexity while maintaining data availability for critical measurements.
3Measurement precision
If all measurement data is retained, then accuracy is maintained, but storage efficiency decreases
Solution Approach 1:
The patent applies prediction accuracy thresholds as a parameter to determine which data to store. By setting thresholds that ensure critical data is retained while allowing compression of predictable data, the system maintains measurement precision for essential parameters while improving overall storage efficiency.
Data Source
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AI summary
A data management system is provided, which includes a data acquiring unit that acquires measurement data generated by measuring a measurement target from each of a plurality of sensors; a data storage unit that stores the acquired measurement data; and a data-amount reducing unit that deletes part of the measurement data acquired from a target sensor based on the measurement data acquired from another sensor among the plurality of sensors to reduce an amount of data to be stored.