Multi-Sensor Data Retention Priorities for Accurate Storage Deletion
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Solution Overview
Problem
Existing data management systems face challenges in maintaining high accuracy when deleting sensor measurement data, as they often focus on deleting data based on a single criterion like degree of change, failing to efficiently manage data from multiple sensors and prioritize data retention from high-importance sensors.
Innovation Solution
A data management system that acquires and records measurement data from multiple sensors, sets priorities based on variance or variation of the data, and selectively deletes data from sensors with lower priorities to maintain high-importance data, allowing for efficient data management and storage optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If sensor data is deleted based on a single criterion like degree of change, then data storage capacity is optimized, but data accuracy and reliability deteriorate
Solution Approach 1:
The patent segments the data deletion decision into multiple independent evaluation criteria: degree of change in evaluation index, sensor reliability, and data freshness. Each criterion is calculated separately and then integrated to determine the final saving priority order, allowing comprehensive assessment rather than relying on a single criterion
Solution Approach 2:
The patent changes the parameter basis for deletion from a single criterion to multiple parameters including evaluation index variation, sensor reliability metrics, and data age. By introducing reliability as a separate parameter and combining it with degree of change, the system maintains data accuracy while optimizing storage
2Reliability
If data from all sensors is retained equally, then data accuracy is maintained, but storage efficiency deteriorates
Solution Approach 1:
The patent applies local quality by assigning different saving priorities to different sensors based on their individual characteristics. Each sensor's data is evaluated separately using the same multi-criteria framework, allowing high-reliability sensors to retain more data while low-reliability sensors have their data deleted more aggressively, optimizing storage efficiency selectively
Solution Approach 2:
The system implements a selective discarding mechanism where data from sensors with low reliability or stale data is deleted first, while data from high-reliability sensors with significant changes is preserved. This selective discarding maintains data accuracy by keeping critical information while improving storage efficiency by removing redundant data
3Ease of operation
If data deletion is performed without priority ordering, then processing simplicity is maintained, but data quality deteriorates
Solution Approach 1:
The patent performs preliminary calculations of the evaluation index, its degree of change, and sensor reliability metrics before the deletion decision is made. By pre-computing these parameters and establishing the saving priority order in advance, the system maintains processing simplicity while ensuring data quality through structured evaluation
Data Source
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AI summary
[Solution to Problem] A data management system is provided, which includes a data acquiring unit for acquiring, from each of a plurality of sensors, measurement data generated by measuring a measurement target, a data storage unit for recording the measurement data, a priority setting unit for setting a priority to be given to each of the plurality of sensors, based on the measurement data acquired from each of the plurality of sensors, a data selecting unit for selecting data to be deleted from among the measurement data, according to the priority for each of the plurality of sensors, and a data deleting unit for deleting the data to be deleted.