Atmospheric Sensor Calibration via Verification Reference
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Solution Overview
Problem
Existing atmospheric pollution monitoring systems face challenges with high maintenance costs and downtime due to the need for frequent calibration of environmental sensors, which requires skilled technicians and involves deinstallation and laboratory fine-tuning, leading to inefficiencies and increased expenses.
Innovation Solution
A system comprising mobile appliances with atmospheric sensors installed on vehicles and remote processing units, utilizing verification sensors and automatic calibration units to compare and adjust sensor readings in real-time, allowing for targeted calibrations and reducing the need for frequent maintenance, thereby minimizing downtime and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If environmental sensors are used for continuous atmospheric pollution monitoring, then measurement precision is improved, but device complexity and maintenance requirements increase due to the need for frequent calibration
Solution Approach 1:
The system enables sensors to perform self-diagnosis and self-calibration by comparing their readings with reference sensors. The processing unit automatically identifies sensors requiring calibration and triggers recalibration without human intervention, allowing the monitoring system to maintain precision while reducing calibration complexity.
Solution Approach 2:
The system implements continuous feedback by comparing sensor readings against reference values from verification sensors. When drift is detected beyond threshold values, the system automatically initiates calibration procedures and tracks calibration status, creating a closed-loop system that maintains measurement precision while automating the calibration process.
2Reliability
If frequent calibration of atmospheric sensors is performed to maintain measurement accuracy, then reliability is improved, but loss of time increases due to sensor deinstallation and laboratory fine-tuning
Solution Approach 1:
The system performs preliminary calibration actions by continuously monitoring sensor drift and automatically initiating calibration procedures when threshold values are exceeded. This prevents significant drift accumulation and reduces the frequency of extensive laboratory calibrations, maintaining reliability while minimizing time loss.
Solution Approach 2:
The automatic calibration system enables sensors to be recalibrated in-situ without deinstallation. The processing unit triggers calibration sequences that adjust sensor readings based on comparisons with reference sensors, eliminating the need for time-consuming laboratory fine-tuning while maintaining measurement reliability.
3Measurement precision
If skilled technicians are deployed for sensor calibration to ensure accuracy, then measurement precision is maintained, but loss of substance increases due to high maintenance costs
Solution Approach 1:
The system replaces skilled technician intervention with automated self-calibration capabilities. The processing unit automatically compares sensor readings with reference sensors, identifies calibration needs, and executes recalibration procedures, maintaining measurement precision while eliminating the need for expensive skilled maintenance personnel.
Solution Approach 2:
The continuous feedback mechanism automatically detects sensor drift and triggers calibration only when necessary, based on predefined threshold values. This targeted approach maintains measurement accuracy while reducing unnecessary calibration operations, thereby lowering maintenance costs without requiring skilled technicians for routine checks.
4Productivity
If multiple atmospheric sensors are deployed to improve monitoring coverage, then productivity is improved, but device complexity increases leading to more sensors requiring calibration
Solution Approach 1:
The processing unit performs multiple functions: it collects data from all sensors, compares readings with reference sensors, identifies drift, triggers calibration, and manages the entire monitoring network. This centralized multi-functional approach handles increased system complexity from multiple sensors while maintaining improved monitoring coverage and productivity.
Data Source
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AI summary
The system (1) for monitoring atmospheric pollution, comprising at least a mobile appliance (2) fitted on a vehicle (V) and a remote processing unit (3), wherein the mobile appliance (2) comprises a location unit (6) suitable for detecting geographic coordinates (G) of the vehicle (V). an atmospheric sensor (7) suitable for detecting at least an atmospheric pollution value (I), a transmission unit (8) suitable for transmitting the geographic coordinates (G) and the atmospheric pollution value (I), and wherein the remote processing unit (3) comprises a receiving unit (10) suitable for receiving the geographic coordinates (G) and the atmospheric pollution value (I), a processing unit (11) suitable for processing the geographic coordinates (G) and the atmospheric pollution value (I) for monitoring the atmospheric pollution inside a geographic area, and wherein the system (1) comprises: - at least a verification sensor (7, 13); - comparison means (16) between the atmospheric pollution value (I) and a reference pollution value (Ir) detected by the verification sensor (7, 13), substantially in correspondence to the same geographic coordinates (G); - signalling means (17) operatively associated with the comparison means (16) and suitable for signalling the need for a calibration of the atmospheric sensor (7) of the mobile appliance (2), if the atmospheric pollution value (I) substantially differs from the reference pollution value (Ir).