Network Signal Correlation for Noise Source Identification
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Solution Overview
Problem
Existing information processing devices cannot effectively derive correlations between noise and normal operation of candidate noise generation sources in a loopback system, making it difficult to specify noise sources within a network.
Innovation Solution
An information processing device acquires time-series data of signal quality and control data, using a correlation derivation unit to establish a correlation value between variations in these data sets, allowing for the identification of noise generation sources within the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a loopback system with switching connection and disconnection of candidate generation sources is used to detect noise, then noise detection capability is improved, but device complexity and operation time increase significantly
Solution Approach 1:
The invention extracts only the necessary information (control data from normal operation) to establish noise correlations, eliminating the need for complex loopback switching systems. By taking out only the essential control data acquisition function, the system achieves noise source identification without the complexity of physical switching mechanisms.
Solution Approach 2:
The invention replaces the mechanical switching system (physical connection/disconnection of cables) with an information processing approach (acquiring and analyzing control data). This substitution eliminates mechanical complexity while maintaining the ability to identify noise sources through correlation analysis of operational data.
2Measurement precision
If loopback testing with switching is performed to specify noise sources, then measurement accuracy is improved, but loss of time increases due to required testing procedures
Solution Approach 1:
The invention performs preliminary action by acquiring control data during normal operation before any noise detection is needed. This advance data collection eliminates the need for subsequent testing procedures, as the correlations are already established and can be used immediately for noise source identification.
Solution Approach 2:
The system maintains continuous acquisition of control data during normal operation, ensuring that noise correlations are continuously updated without interrupting the operational process. This continuous action eliminates the need for separate testing phases, as noise detection integrates seamlessly with normal system operation.
3Device complexity
If control data from normal operation is used to derive noise correlations, then device complexity is reduced, but measurement precision may be insufficient without active testing
Solution Approach 1:
The system uses its own operational control data to identify noise sources, making the system self-diagnostic. By leveraging data already generated during normal operation, the system achieves noise detection capability without requiring external testing equipment or complex diagnostic procedures.
Solution Approach 2:
The invention implements feedback by continuously monitoring control data and using it to establish correlations with noise signals. This feedback mechanism ensures that the system can accurately identify noise sources based on actual operational patterns, maintaining measurement precision while keeping the system simple.
Data Source
AI summary
The present invention acquires, from control data for an apparatus to be controlled in a normal operation, a relationship between a noise included in a reception signal from a cable and the apparatus to be controlled. An information processing device (13) is provided with a correlation derivation unit (13212, 13212d) for deriving a correlation value between a variation of a time-series data (1331) of a noise included in a signal input through a cable in a network and a variation of a time-series data (1332) of control data of an apparatus to be controlled (11, 16) in the network.