Population State Detection Using Encoder-Decoder Anomaly Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing population estimation methods fail to accurately detect anomalies due to not considering population changes, leading to insufficient detection of sudden events or high population concentrations during disasters.
Innovation Solution
A population state determination system utilizing an encoder-decoder model to compress and reconstruct population data, comparing input and output to determine population states, and generating a determination criterion based on first and second population information for accurate anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If statistical methods based on average and variance are used for population anomaly detection, then the detection process is simple, but the detection accuracy is insufficient because population changes are not considered
Solution Approach 1:
The patent transforms population data from static counts to dynamic change rates by calculating the difference between current and historical population data. This parameter transformation enables the system to detect anomalies based on population dynamics rather than absolute numbers, resolving the contradiction between simple detection and accurate detection.
Solution Approach 2:
The system pre-calculates and stores historical population data and establishes baseline population change patterns before actual anomaly detection occurs. This preliminary preparation of reference data allows for accurate real-time anomaly detection without complex calculations during the detection phase itself.
2Device complexity
If conventional population estimation methods are used, then the system is simple to implement, but it cannot detect sudden events or high population concentrations during disasters
Solution Approach 1:
The patent implements dynamic population change rate calculation by continuously comparing current population data with historical data. This dynamic approach allows the system to adapt to changing conditions and detect sudden population changes during disasters, improving reliability without requiring complex system architecture.
Solution Approach 2:
The system introduces population change rate as an intermediary metric between raw population counts and anomaly detection. This intermediary transformation enables the detection of sudden events and disaster conditions while maintaining relative system simplicity.
Data Source
AI summary
A population state determination system includes an acquisition unit configured to acquire population information indicating a population in a time series in an area that is a population state determination target, a model calculation unit configured to perform calculation by inputting the population information to a pre-stored encoder-decoder model for compressing and reconstructing input data and obtain an output from the encoder-decoder model, a determination unit configured to determine a state of the population in the area by comparing the population information with the output, and a determination criterion generation unit configured to generate a determination criterion for use in the determination, wherein the determination criterion generation unit performs calculation by inputting population information for determination criterion generation to an encoder-decoder model for determination criterion generation stored in advance and generates a determination criterion.