Water Flow Cutoff Using Predictive Quality Index Monitoring
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Solution Overview
Problem
Maintaining good water quality for human consumption is challenging due to the presence of microbes and chemicals, posing threats to human health and limiting food production and ecosystem functions, with existing systems struggling to effectively predict and prevent water-borne ailments.
Innovation Solution
A system utilizing IoT devices and machine learning to monitor water quality, calculate a fluid quality index, and trigger proactive corrective measures by stopping water supply when quality thresholds are breached, integrating cloud platform services and standardized indices to predict and prevent contamination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional water quality monitoring methods are used, then system complexity is reduced, but the ability to predict and prevent water-borne ailments deteriorates
Solution Approach 1:
The system performs preliminary analysis of water quality parameters using machine learning models to predict potential contamination events before they occur. The FL fluid quality index calculation and predictive analytics enable early detection of water quality degradation trends, allowing preventive actions to be taken before harmful contamination levels are reached.
Solution Approach 2:
The patent introduces an intermediary FL fluid quality index that synthesizes multiple water quality parameters into a single comprehensive metric. This index acts as a mediator between raw sensor data and decision-making processes, simplifying the interpretation of complex water quality data while maintaining high prediction accuracy through machine learning algorithms.
2Reliability
If real-time water quality monitoring is implemented, then water safety is improved, but response time to quality changes is reduced
Solution Approach 1:
The system implements continuous feedback loops where water quality parameters are monitored in real-time, analyzed by machine learning models, and used to dynamically adjust the FL fluid quality index. This feedback mechanism enables the system to rapidly detect quality changes and trigger appropriate responses, minimizing the time between contamination events and corrective actions.
Solution Approach 2:
The patent employs dynamic threshold adjustment and adaptive monitoring frequencies based on real-time water quality conditions. When quality parameters approach critical levels, the system automatically increases monitoring intensity and accelerates response actions, optimizing the balance between detection sensitivity and response time.
3Measurement precision
If comprehensive fluid parameter monitoring is performed, then measurement precision is improved, but the quantity of data to be processed increases
Solution Approach 1:
The system extracts and focuses on the most critical water quality parameters that have the highest impact on fluid safety and contamination prediction. By identifying and prioritizing key parameters through machine learning analysis, the system maintains high measurement precision for essential metrics while reducing the overall data volume that requires processing and storage.
Solution Approach 2:
The patent transforms multiple detailed water quality parameters into the simplified FL fluid quality index through mathematical relationships and machine learning models. This parameter transformation maintains the predictive power and precision of comprehensive monitoring while reducing data complexity and processing requirements through dimensionality reduction.
Data Source
AI summary
A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include accepting fluid parameter data about a fluid and identifying at least one safety threshold for the fluid. The operations may further include calculating a fluid quality index for the fluid based on the fluid parameter data and analyzing the fluid quality index against the at least one safety threshold to achieve fluid quality testing data. The operations may also include leveraging the fluid quality testing data to control a fluid flow.