Multi-Sensor Water Monitoring With Abnormality Correction
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Solution Overview
Problem
Existing sensor devices struggle to accurately measure multiple water-related items simultaneously due to limitations in detecting abnormalities and correcting measurement data in real-time, leading to potential inaccuracies and inefficiencies in water treatment processes.
Innovation Solution
An information processing apparatus that acquires and analyzes data from multiple sensors to estimate and correct abnormalities, using a trained model to identify failed or deviated sensors and adjust measurement data based on past and current data, ensuring accurate and continuous monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a pH meter is determined to be abnormal only at the time of calibration, then the device complexity is reduced, but the measurement precision deteriorates because the pH may not be accurately measured between calibration points
Solution Approach 1:
The system implements continuous feedback by monitoring measurement data from multiple sensors in real-time, comparing current readings with historical data and expected ranges, and automatically detecting abnormalities without requiring frequent calibration checks. This continuous monitoring feedback loop maintains measurement precision while reducing the need for complex periodic calibration mechanisms.
Solution Approach 2:
The sensor device performs self-diagnosis by automatically analyzing its own measurement data to detect abnormalities. The system uses its own operational data and comparison with expected patterns to identify when sensors are malfunctioning, eliminating the need for external calibration verification and enabling continuous self-validated accurate measurement.
2Adaptability or versatility
If multiple sensors are used to measure different water quality items, then the adaptability is improved, but the reliability deteriorates due to potential sensor failures and calibration deviations
Solution Approach 1:
The system merges data from multiple sensors with different measurement functions into a unified analysis framework. By combining measurement data from pH, ORP, and other sensors, the system achieves comprehensive water quality monitoring while using cross-validation techniques to maintain reliability - if one sensor shows abnormality, the system can detect it through inconsistencies in the combined data set.
Solution Approach 2:
The system dynamically changes monitoring parameters based on detected abnormalities. When a sensor deviation is detected, the system adjusts which parameters are monitored more closely and uses historical data to establish dynamic baseline ranges, allowing the multi-parameter system to adapt and maintain reliability even when individual sensors drift or fail.
3Ease of operation
If sensor abnormalities are not detected in real-time, then the ease of operation is improved, but the loss of information increases due to inaccurate measurement data continuing to be used
Solution Approach 1:
The system maintains continuous useful action by constantly analyzing measurement data streams in real-time without interruption. The automatic abnormality detection operates continuously alongside normal measurements, ensuring that accurate information is never lost even as the system maintains ease of operation through automated monitoring and detection processes.
Data Source
AI summary
An information processing apparatus includes processing circuitry. The processing circuitry acquires measurement data from a plurality of sensors configured to measure different items. The processing circuitry estimates whether or not there is an abnormality in any of the plurality of sensors, based on the acquired measurement data of the items and measurement data of the items acquired in the past. The processing circuitry corrects measurement data measured by a sensor estimated to have an abnormality, based on the acquired measurement data of the items and the measurement data of the items acquired in the past.