Behavior Estimation Using Power Frequency Thresholds
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Solution Overview
Problem
Existing methods fail to accurately estimate a consumer's behavior status without human sensors, making it difficult to manage power consumption and provide services based on power consumption information.
Innovation Solution
A behavior estimation apparatus that calculates power thresholds from frequency data to differentiate between 'base power', 'long term behavior power', and 'short term behavior power' using a system comprising a storage unit, calculation units, and an estimation unit, which estimates behavior status by comparing power values with these thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If power consumption data is analyzed to estimate behavior status, then power consumption management can be achieved, but measurement precision of behavior status is insufficient
Solution Approach 1:
The patent segments power consumption data into multiple frequency components using Fast Fourier Transform (FFT). By dividing the power consumption signal into different frequency bands and analyzing each segment separately, the system can identify distinct behavior patterns more accurately. This segmentation allows differentiation between various behavior states (e.g., sleeping, working, leisure) based on characteristic frequency signatures of power consumption patterns.
Solution Approach 2:
The patent introduces frequency domain analysis as an intermediary between raw power consumption data and behavior status estimation. Instead of directly interpreting time-domain power data, the system transforms it into the frequency domain where behavior-specific patterns become more distinguishable. This intermediary transformation enhances the information extraction capability and improves behavior status recognition accuracy.
2Measurement precision
If human sensors are used to detect behavior status, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent enables the power consumption measurement system to automatically infer behavior status information without requiring separate human sensors. The existing power consumption data, when analyzed through frequency domain methods, contains sufficient information to self-determine behavior status. This self-service approach eliminates the need for additional sensors while maintaining accurate behavior detection.
Solution Approach 2:
The patent replaces physical human sensors (mechanical/detector systems) with signal processing algorithms. Instead of using sensors to directly detect human presence or behavior, the system uses mathematical transformations (FFT, frequency analysis) to extract behavior information from power consumption patterns. This substitution reduces hardware complexity while achieving comparable or superior detection accuracy.
3Productivity
If detailed behavior status information is obtained, then service provision quality improves, but loss of time for data processing increases
Solution Approach 1:
The patent performs preliminary frequency domain transformation and identifies characteristic frequency patterns associated with different behavior states in advance. By pre-establishing the relationship between frequency components and behavior types, the system can quickly match observed patterns to known behaviors without extensive real-time processing. This preliminary analysis framework enables rapid behavior status determination while maintaining detailed information accuracy.
Data Source
AI summary
According to one embodiment, a behavior estimation apparatus includes a storage unit, a first calculation unit, a second calculation unit, and an estimation unit. The storage unit stores first data collecting power values consumed by a consumer in a period. The first calculation unit calculates second data representing a frequency of each power value by using the first data. The second calculation unit calculates a first threshold to divide a first power value from a second power value which is larger than and next to the first power value, among power values corresponding to maximal values of frequencies included in the second data. The estimation unit obtains a power value consumed by the consumer in an estimating period, and estimates the consumer's behavior status in the estimating period by using the power value and the first threshold.