Sensor Grouping for Time Series Data Storage Optimization
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Solution Overview
Problem
Current methods for processing time series data from multiple sensors have low utilization efficiency, particularly when dealing with massive data from low-frequency sampling sensors, as they require significant buffer space and inefficiently store data per sensor rather than per sensor group.
Innovation Solution
Divide sensors into groups and store time series data in a first database by sensor group, allowing for efficient temporary storage and reorganization, then transfer data to a second database for persistent storage, using compression techniques to reduce storage space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If time series data from multiple sensors are stored individually per sensor in a temporary database, then data retrieval for each sensor is straightforward, but buffer utilization efficiency is low and significant storage space is required
Solution Approach 1:
The patent merges time series data from multiple sensors into a single database record by sensor group, combining what would traditionally be stored as separate records. This consolidation reduces the total number of database records and improves buffer utilization efficiency while maintaining efficient data access through the re-organizing step that retrieves data by individual sensors when needed
2Productivity
If massive time series data from low-frequency sampling sensors are stored temporarily per sensor, then each sensor's data is easily accessible, but buffer utilization efficiency is significantly reduced
Solution Approach 1:
The patent segments sensors into multiple sensor groups and stores data from each sensor group in separate database records. This segmentation allows efficient bulk storage and processing while the data re-organizing module can efficiently retrieve and reassemble data by individual sensors, balancing buffer utilization with data accessibility without requiring complex real-time reorganization structures
3Reliability
If time series data are stored in individual database records for each sensor, then data integrity per sensor is maintained, but storage space efficiency is low
Solution Approach 1:
The patent combines time series data from multiple sensors into consolidated database records organized by sensor groups, reducing the total number of database records and improving storage space efficiency. Data integrity is maintained through the structured format that preserves individual sensor data within the consolidated records and the re-organizing capability that can extract individual sensor data when needed
Data Source
AI summary
Processing time sequence data for multiple sensors, wherein the multiple sensors are divided into multiple sensor groups and each data comprises a time stamp and a value associated with the timestamp. The method comprises: receiving time series data from each sensor; assigning the time series data received to a sensor group to which the sensor belongs; storing time series data in a first database of a first memory, such that multiple time series data assigned to the same sensor group in the multiple sensor groups are stored in at least one database record of the first database; obtaining the time series data of each sensor among the multiple sensors from the first database; storing time series data in a second database of a second memory, such that the multiple time series data from the same sensor are stored in at least one database record of the second database.