Sensor Data Processing Using Feature Vector Identifier Tables
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Solution Overview
Problem
High-dimensional feature vectors in sensor data processing for situation recognition increase system memory and calculation demands, leading to complexity and difficulties in real-time processing, especially when multiple sensors are involved.
Innovation Solution
Generating a feature vector identifier table by converting feature vectors into fixed-length bit series and using similarity comparisons to determine situation recognition information, reducing the need for high-dimensional processing and simplifying system complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-dimensional feature vectors are used for situation recognition, then recognition accuracy is improved, but system memory and calculation demands increase
Solution Approach 1:
The patent segments the high-dimensional feature vector processing into two parts: (1) pre-processing offline to create a reference database with clustered situation patterns, and (2) real-time processing by comparing sensor data against the pre-processed reference. This segmentation reduces real-time computational burden while maintaining recognition accuracy.
Solution Approach 2:
The patent performs preliminary clustering and pattern recognition offline to build a reference database before real-time operation. By pre-processing the data and organizing it into situation-based clusters, the system eliminates the need for complex real-time clustering operations, reducing memory and calculation demands during actual situation recognition.
2Measurement precision
If multiple sensors are used for situation recognition, then recognition accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent merges data from multiple sensors into a unified feature vector representation. By combining multi-sensor data into a single integrated feature vector that can be processed through the same clustering and recognition pipeline, the system maintains high recognition accuracy while reducing processing complexity compared to handling each sensor separately.
3Measurement precision
If dynamic feature vectors are processed in real-time, then situation recognition is improved, but real-time processing becomes difficult
Solution Approach 1:
The patent performs preliminary clustering and pattern establishment offline using historical sensor data. The resulting reference database with pre-defined situation patterns and cluster centers is stored for real-time use. During real-time operation, the system only needs to compare incoming feature vectors against the pre-processed reference, enabling fast situation recognition without complex real-time clustering operations.
Data Source
AI summary
In a sensor network, a sensor data processing apparatus generates a feature vector identifier table by classifying feature vector identifiers of a plurality of situation information determination reference data to be a reference of situation determination according to a sensor type index and a feature vector identifier set index of the plurality of situation information reference data. When the sensor data processing apparatus receives sensor data, the sensor data processing apparatus generates a feature vector identifier of the sensor data and extracts a sensor type index and a feature vector identifier set index of a feature vector identifier most similar to the feature vector identifier of sensor data with reference to a feature vector identifier table, and generates situation recognition information using the extracted sensor type index and feature vector identifier set index.