Machine Learning Sanitary Facility Management for Water Optimization
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Solution Overview
Problem
Current sanitary facility management systems require significant human effort to optimize water consumption, making it difficult to efficiently manage water usage in larger facilities, and existing leak detection systems rely on complex setups and human intervention for effective operation.
Innovation Solution
Implementing a machine learning-based sanitary facility management system that uses artificial neural networks and expert systems to analyze data from sensors, learn usage patterns, and autonomously optimize water consumption and detect leaks, reducing the need for human intervention and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional water consumption monitoring systems are used in sanitary facilities, then basic data collection is achieved, but significant human effort is required to evaluate statistics and make optimization decisions
Solution Approach 1:
The system enables self-service through machine learning models that automatically analyze water consumption data, detect leaks, identify usage patterns, and generate optimization recommendations without requiring human evaluation of statistics. The sanitary facility management system performs autonomous decision-making based on learned patterns from historical data.
Solution Approach 2:
The patent replaces manual mechanical evaluation processes with automated computational systems. Machine learning algorithms substitute human analysts in evaluating water consumption statistics, detecting anomalies, and making optimization decisions, thereby eliminating the need for significant human effort while maintaining or improving decision quality.
2Productivity
If manual evaluation of water consumption statistics is performed, then water conservation decisions can be made, but the effort required is significant and almost unfeasible for larger sanitary facilities
Solution Approach 1:
Manual evaluation processes are completely replaced by automated machine learning systems that continuously analyze water consumption data, detect leaks, and generate optimization recommendations instantaneously. This substitution eliminates the time-consuming manual analysis while improving the speed and accuracy of water conservation management.
Solution Approach 2:
The system enables continuous automated monitoring and analysis of water consumption patterns without interruption. Machine learning models process data in real-time, providing continuous optimization recommendations rather than periodic manual reviews, thereby maximizing water conservation efficiency while minimizing time investment.
3Loss of substance
If resources for water conservation are not fully utilized due to manual evaluation limitations, then water consumption cannot be optimized effectively, but implementing automated systems requires additional technical complexity
Solution Approach 1:
The patent replaces complex manual evaluation processes with automated machine learning systems that efficiently utilize available water conservation resources. The system automatically identifies optimization opportunities, detects leaks, and generates actionable recommendations, thereby fully utilizing conservation resources without requiring proportionally complex manual intervention.
Data Source
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AI summary
The present invention comprises a sanitary facility management method with at least one sanitary installation which is coupled to a water supply and/or integrated into a water flow, and on which at least one operating value of the at least one sanitary installation can be detected, 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 machine learning system and/or a system comprising an artificial neural network and/or an expert system.