Ultrasonic Water Meter Deep Learning Impurity Detection
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Solution Overview
Problem
Current water quality detection systems lack the precision and intelligence needed to accurately measure changes in water quality at the metering level, particularly in detecting dissolved impurities, which can lead to noncompliance issues for water utilities.
Innovation Solution
The implementation of a method and system that utilizes ultrasonic sensors to classify water quality based on time-of-flight (ToF) data changes, combined with a sequential learning unit, such as a machine learning algorithm, to identify and classify impurities like TDS, pH, chlorine residual, turbidity, and total organic carbon values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional water sensing sensors and alarm systems are used, then water quality monitoring is provided, but accurate measurement of changes in water quality cannot be achieved
Solution Approach 1:
The patent replaces traditional mechanical water quality sensors with ultrasonic sensing technology. The ultrasonic sensor measures time-of-flight (ToF) of sound waves through water, and a sequential learning unit (machine learning algorithm) processes this data to detect impurities. This substitution of mechanical sensing with acoustic field-based sensing and intelligent processing enables accurate detection of dissolved impurities that traditional sensors cannot detect.
2Adaptability or versatility
If multiple water sensing sensors are installed, then water quality parameters can be monitored, but intelligent classification and training capability are lacking
Solution Approach 1:
The patent implements a sequential learning unit that enables the system to self-train and adapt to different water quality conditions. The machine learning algorithm continuously learns from incoming ultrasonic ToF data, automatically classifying different types of impurities (TDS, pH, chlorine residual, turbidity, total organic carbon) without requiring manual reconfiguration. This self-service capability allows the system to improve its detection accuracy over time while handling multiple water quality parameters.
3Reliability
If conventional sensor systems are used, then basic water quality monitoring is achieved, but detection of dissolved impurities remains insufficient
Solution Approach 1:
The patent introduces an intermediary processing layer between the ultrasonic sensor and the output alarm system. The sequential learning unit acts as this intermediary, receiving raw ultrasonic ToF measurements and transforming them into classified impurity detections. This intermediary intelligence layer enables sophisticated detection of dissolved impurities while keeping the physical sensor structure relatively simple, as only ultrasonic sensors are needed without complex mechanical sensor arrays.
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
This approach enables accurate and real-time detection of water impurities, allowing for timely action by consumers and utilities, thereby improving water quality monitoring and compliance.
Implementation Method 1
utilizes ultrasonic sensors to classify water quality based on time-of-flight (ToF) data changes
Implementation Method 2
obtaining the data indicative of ultrasonic time-of-flight change behavior from a plurality of ultrasonic sensors
Data Source
AI summary
Methods and systems for detecting water quality, can involve classifying the quality of water using a water meter with respect to data indicative of ultrasonic time-of-flight (ToF) change behavior due to a mixed or combination of impurities in the water, and utilizing a sequential learning unit for classification of impurities in the water. The data indicative of ultrasonic time-of-flight change behavior can be obtained from one or more ultrasonic sensors associated with the water meter. The impurities in the water can be classified by the sequential learning unit as water quality parameters including one or more of, for example, TDS (Total Dissolved Solids), ph level, chlorine residual data, turbidity information, and total organic carbon values. The data can be transmitted to a user through a radio frequency frame.