On-Device Data Analysis System with Cloud Offloading
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Solution Overview
Problem
The increasing size of measurement data collected from sensors and equipment often exceeds the on-device resource usage rate, leading to high loads on cloud servers and increased hardware costs, necessitating a solution to reduce cloud server loads and enable real-time analysis.
Innovation Solution
An on-device processing device that collects and pre-processes measurement data, performing noise removal and analysis within predetermined resource usage ranges, and transmitting excess data to a cloud server for further processing, utilizing artificial intelligence models for abnormal data handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If measurement data is transmitted to cloud server for processing, then analysis capability is improved, but cloud server load increases and hardware costs increase
Solution Approach 1:
The system segments data processing into two parts: on-device pre-processing for basic filtering and cloud server analysis for complex processing. The on-device processing device performs preliminary processing to reduce data volume before transmission to the cloud server, thereby reducing cloud server load while maintaining analysis capability.
Solution Approach 2:
The on-device processing device performs preliminary actions including noise removal and data filtering before transmitting data to the cloud server. This preliminary processing reduces the amount of data that needs to be processed by the cloud server, effectively reducing cloud server load and hardware costs.
2Productivity
If on-device processing capacity is increased, then processing speed is improved, but device complexity increases
Solution Approach 1:
The system extracts complex processing tasks from the on-device processing device and relocates them to the cloud server. The on-device device only performs simple pre-processing operations, which simplifies the device architecture while maintaining high processing speed through cloud-based complex analysis.
Solution Approach 2:
The cloud server acts as an intermediary that handles complex processing tasks. This intermediary approach allows the on-device processing device to remain simple while still achieving high processing speed through the cloud server's computational power.
3Quantity of substance
If data filtering is performed on-device, then data transmission volume is reduced, but processing time increases
Solution Approach 1:
The on-device processing device performs partial filtering actions using simple algorithms to reduce data transmission volume. This partial action is sufficient to significantly reduce data volume while the processing time impact is minimized because the filtering is performed only on essential data points.
Data Source
AI summary
An on-device based data analysis system and a method are proposed. According to specific examples of the system and method, when a size of measurement data collected from sensors, processes, or various equipment according to a user request is greater than or equal to an upper limit of a predetermined range of an on-device resource usage rate, the collected measurement data is transmitted to a cloud server to perform noise removal, pre-processing, and analysis on the measurement data on a cloud-basis, and when the size of the measurement data is within the predetermined range of the on-device resource usage rate or is less than a lower limit, the noise removal, pre-processing, and analysis is performed on the measurement data on an on-device basis, so that distributed processing between the on-device processing device and the cloud server is performable, whereby loads on the cloud server may be reduced.