Sensor Data Image Pattern Conversion for Abnormal Situation Detection
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Solution Overview
Problem
Conventional sensing data analysis methods are slow in detecting abnormal situations in IoT environments due to their reliance on predefined thresholds, which do not account for spatial correlations among sensor data, making it difficult to quickly respond to critical events like fires or process management issues.
Innovation Solution
An abnormal situation detection apparatus and method that converts sensor data into image patterns using vector normalization, allowing for the generation of learning models to quickly identify anomalies by analyzing both normal and abnormal situation data in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional sensing data analysis methods using predefined thresholds are used, then the system is simple to implement, but the detection speed and accuracy of abnormal situations is slow
Solution Approach 1:
The patent transforms sensor data from raw numerical values into image patterns through parameter changes including normalization, vectorization, and spatial arrangement. This transformation enables the use of image processing techniques and deep learning models that can rapidly detect abnormal patterns, significantly improving detection speed while maintaining manageable system complexity through automated processing pipelines.
Solution Approach 2:
The patent replaces conventional threshold-based mechanical comparison methods with intelligent image processing and machine learning systems. By substituting simple threshold checks with neural network-based image pattern recognition, the system achieves faster and more accurate abnormal situation detection, resolving the contradiction between detection speed and system complexity.
2Measurement precision
If comprehensive analysis of sensing data from all sensor devices is performed to determine situation, then the accuracy of abnormal detection is improved, but the time required to determine abnormal situation increases
Solution Approach 1:
The patent adds a spatial dimension by arranging sensor data in image patterns, where sensors are positioned according to their physical locations. This dimensional transformation enables parallel processing of spatial relationships and correlations among multiple sensors simultaneously, maintaining high detection accuracy while reducing analysis time through efficient image processing algorithms.
Solution Approach 2:
The patent performs preliminary actions by pre-processing sensor data into standardized image patterns and pre-training detection models with normal and abnormal situation patterns. This preliminary preparation enables rapid real-time detection without requiring comprehensive analysis during critical events, thus maintaining high accuracy while minimizing analysis time.
3Productivity
If sensor data is converted into image patterns and learning models are generated, then rapid detection of abnormal situations is enabled, but the complexity of data processing increases
Solution Approach 1:
The patent implements self-service through automated pipelines where sensor data is automatically transformed into image patterns, models are automatically trained and updated, and detection results are automatically generated. This automation reduces manual intervention and simplifies operation despite the underlying processing complexity, thereby improving detection efficiency while managing system complexity through autonomous operation.
Data Source
AI summary
An apparatus and method for abnormal situation detection are disclosed. An abnormal situation detection apparatus can map first sensor data among sensor data transmitted from a plurality of sensors into a vector value, convert it into first situation information in the form of an image pattern, and generate a learning model using the first situation information and an abnormal situation reference range. In addition, the abnormal situation detection apparatus can convert second sensor data among sensor data transmitted from a plurality of sensors into a form that can be input to the learning model, and determine whether an abnormal situation occurs by applying the converted second data to the learning model.