AI Fluid Hue Determination Without Optical Fiber Calibration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for determining the color of fluids in fluid distribution systems, such as paint, require manual calibration of light sensors due to optical fiber attenuation, which is complex and time-consuming.
Innovation Solution
A calculation unit using a light source, light sensor, and optical fibers to capture light signals, combined with a pre-trained artificial intelligence model to determine fluid hue, eliminating the need for manual calibration and simplifying the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration is performed to account for optical fiber attenuation, then measurement precision is improved, but loss of time increases and device complexity increases
Solution Approach 1:
The system performs preliminary calibration by training an artificial intelligence model offline with labeled spectral data before actual fluid color determination. This pre-trained model automatically compensates for optical fiber attenuation effects during runtime without requiring manual calibration operations, thus eliminating time loss while maintaining measurement precision.
Solution Approach 2:
The patent replaces the manual mechanical calibration process with an automated artificial intelligence-based system. The AI model processes spectral signals and automatically compensates for attenuation effects, substituting human-operated mechanical calibration with intelligent automated computation, thereby reducing both time loss and operational complexity.
2Measurement precision
If manual calibration is performed to account for optical fiber attenuation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex manual calibration procedures with an automated artificial intelligence system that automatically compensates for optical fiber attenuation. The AI model processes spectral data and performs attenuation correction without human intervention, significantly reducing device complexity while maintaining measurement precision.
Solution Approach 2:
The system enables self-service by allowing the artificial intelligence model to automatically perform attenuation compensation and fluid color determination without requiring external manual calibration operations. The model adapts to and corrects for system variations autonomously, eliminating the need for complex manual calibration processes.
3Measurement precision
If additional light signal processing steps are implemented, then measurement precision is improved, but device complexity increases and productivity decreases
Solution Approach 1:
The system performs complex spectral analysis and attenuation compensation in advance during the offline training phase of the artificial intelligence model. During actual fluid color determination, the pre-trained model rapidly processes spectral signals without requiring additional real-time processing steps, thus maintaining high measurement precision while maximizing productivity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Provides reliable and efficient fluid color determination without additional processing steps, reducing complexity and resource intensity, and enabling continuous monitoring of fluid purity and change operations.
Implementation Method 1
a light sensor configured to receive a light signal that has been reflected from or transmitted through the fluid, the light signal corresponding to an optical reflection or, respectively, an optical transmission, by the fluid
Implementation Method 2
a light sensor configured to receive a light signal that has been reflected from or transmitted through the fluid, the light signal corresponding to an optical reflection or, respectively, an optical transmission, by the fluid
Implementation Method 3
an optical acquisition fiber configured to carry the light signal to the light sensor
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The present invention relates to a computing unit (50) for a device (14) comprising: - at least one light source (22) configured to emit light in the direction of a fluid; - a light sensor (24) configured to receive a light signal representative of a hue of the fluid flowing in a fluid distribution system (10), the light sensor (24) being further configured to emit quantities representative of a light intensity of the light signal for a given color channel; the computing unit comprising an estimation module (52) configured to determine a variable representative of a hue of the fluid via an artificial intelligence model, each quantity representative of a light intensity being an input variable of the model, an output variable of the artificial intelligence model being the variable representative of a hue of the fluid.