AI Sanitary Facility Control for Water Use and Error Detection

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Solution Overview

Problem

Existing sanitary facility management systems require significant human effort to optimize water consumption, especially in larger facilities, making it difficult to efficiently manage water usage and detect issues in real-time.

Innovation Solution

A sanitary facility management system utilizing machine learning and artificial intelligence to analyze data from sensors, optimize settings, and predict usage patterns, allowing for autonomous decision-making and reduced water consumption.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of substance

If traditional data processing systems are used to manage sanitary facilities, then water consumption can be reduced through monitoring, but significant human effort is required to evaluate statistics and make optimization decisions

Engineering Contradiction:

Improvewater consumptionVSAvoidhuman effort for evaluation and decision-making

Core Design Contradiction:

Loss of substanceVSLoss of time

Solution Approach 1:

The system enables self-service through machine learning algorithms that automatically analyze sensor data, detect anomalies, and optimize water consumption without requiring human intervention. The AI model continuously learns from operational data and autonomously makes decisions about water usage optimization, freeing up human resources while maintaining effective water management.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical system of manual data evaluation and decision-making with an artificial intelligence-based automated system. The machine learning model processes sensor data, identifies patterns, and generates optimization decisions, substituting human cognitive effort with computational algorithms that can process information more efficiently and continuously.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Loss of substance

If manual evaluation of operating statistics is performed to optimize water consumption, then water usage can be controlled, but the process becomes increasingly difficult and impractical in larger sanitary facilities

Engineering Contradiction:

Improvewater consumptionVSAvoidease of management in larger facilities

Core Design Contradiction:

Loss of substanceVSEase of operation

Solution Approach 1:

The system replaces manual evaluation processes with automated machine learning algorithms that can handle large volumes of data from multiple sensors across extensive sanitary facilities. The AI model continuously processes operating statistics, detects anomalies, and generates optimization decisions without human intervention, making facility management scalable and practical regardless of size.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The machine learning model acts as an intermediary between sensor data and management decisions. It automatically translates raw operating statistics into actionable insights and optimization recommendations, eliminating the need for manual data interpretation and making the system equally effective for facilities of any scale.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If traditional monitoring systems are used, then operating values can be recorded and transmitted, but real-time error detection and prompt response are difficult to achieve

Engineering Contradiction:

Improveerror detection capabilityVSAvoidresponse time for error detection

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system replaces traditional threshold-based monitoring with machine learning algorithms that continuously analyze operating patterns and detect anomalies in real-time. The AI model identifies deviations from normal operation immediately, enabling prompt error detection and response without the delays associated with manual evaluation or simple threshold comparisons.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system implements continuous feedback loops where sensor data is constantly fed to the machine learning model, which immediately processes the information and generates alerts or optimization decisions. This real-time feedback mechanism ensures errors are detected and addressed promptly, maintaining high system reliability and enabling rapid response to operational issues.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20220101278A1Sanitary facility management system and sanitary facility management method

Publication Date: 2022.03.31 CAROMA INDUSTRIES LTD

AI summary

The present invention comprises a sanitary facility management system as well as a corresponding sanitary facility management method with at least one sanitary installation, which is coupled to a water supply and/or is included in a water circuit and on which at least one operating value of the at least one sanitary installation can be recorded, a sanitary facility control device connected to the at least one sanitary installation comprising at least one data transmitter and at least one signal receiver and a data processing and signal output system communicating with the sanitary facility control device via the at least one data transmitter and the at least one signal receiver, wherein the data processing and signal output system is a system learning by machine and/or a system comprising an artificial neuronal net and/or an expert system.