Adaptive Data Collection Frequency Using Tag-Based Polling
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Solution Overview
Problem
Existing data collection methods require manual setting of data collection frequencies for each facility and data item, which is time-consuming and inefficient, leading to increased person-hours.
Innovation Solution
A data collection frequency determination device that assigns tag information to each data item and uses an acquisition unit, storage unit, and determination unit to automatically determine data collection frequencies based on past data and associated information, optimizing collection frequencies according to fluctuation ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data collection frequency is manually set for each facility and data item, then data collection accuracy is improved, but person-hours for setting increase significantly
Solution Approach 1:
The system automatically determines optimal data collection frequencies by analyzing historical data fluctuations and communication patterns without requiring manual configuration. The determination unit autonomously sets collection frequencies based on learned patterns from past data, eliminating the need for manual setting while maintaining or improving collection accuracy.
Solution Approach 2:
The system dynamically adjusts data collection frequencies by analyzing statistical parameters of historical data fluctuations. By calculating standard deviations and variance in data changes over time, the system adapts collection frequencies to match actual data volatility, achieving accurate collection without manual intervention.
2Ease of operation
If polling is performed at equal intervals for all data, then communication simplicity is maintained, but accuracy decreases when data fluctuation is wide
Solution Approach 1:
The system transitions from static equal-interval polling to dynamic adaptive polling. Collection intervals are continuously adjusted based on real-time analysis of data fluctuation patterns, allowing the system to maintain simple communication protocols while adapting collection frequency to match actual data volatility requirements.
Solution Approach 2:
The system implements feedback mechanisms by analyzing historical data collection results and communication patterns. This feedback is used to continuously refine and adjust collection frequencies, ensuring accuracy is maintained while communication complexity remains minimal through automated optimization.
3Measurement precision
If polling frequency is increased for all data items, then data collection accuracy is improved, but unnecessary communication increases
Solution Approach 1:
The system applies different data collection frequencies to different data items based on their individual fluctuation characteristics. High-volatility data receives more frequent collection while stable data is collected less frequently, optimizing accuracy for each data type without uniformly increasing communication across all items.
Solution Approach 2:
The system performs data collection at varying frequencies rather than uniform high frequency. By applying partial action (reduced frequency) to stable data and excessive action (increased frequency) only to volatile data, the system achieves overall accuracy improvement while minimizing total communication overhead and energy consumption.
Data Source
AI summary
A data collection frequency determination device determines a frequency of collecting data from a facility device in which tag information for identification is assigned for each item of the data that is necessary. The data collection frequency determination device includes an acquisition unit, a storage unit, and a determination unit. The acquisition unit acquires the tag information from the facility device. The storage unit stores the tag information and first information including a collection frequency for the data. The collection frequency for the data is associated with the tag information. The determination unit determines a collection frequency for the data based on the tag information acquired by the acquisition unit and the first information.