Indoor Water Leak Detection Using Multidimensional Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for detecting and calculating indoor and outdoor water leaks are inaccurate and labor-intensive, often requiring manual inference and are prone to false positives or false negatives, which can lead to significant water waste and costly pipe replacements.
Innovation Solution
An indoor water leak detection and type identification device using multidimensional data that employs machine learning techniques, including deep learning and ensemble learning, to analyze water usage patterns and classify leaks, thereby improving the accuracy of leak detection and type identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional listening devices or simple sensor systems are used for water leak detection, then the system complexity is low, but the detection accuracy and reliability are insufficient, leading to false positives or false negatives
Solution Approach 1:
The patent transitions from one-dimensional simple sensor data to multi-dimensional data analysis by incorporating time-series water usage data, consumer information, and facility information. This dimensional expansion enables the machine learning model to capture complex leak patterns and differentiate between actual leaks and normal usage variations, significantly improving detection reliability.
Solution Approach 2:
The patent replaces conventional mechanical listening devices with an intelligent system that uses machine learning algorithms to analyze water usage data patterns. This substitution eliminates the need for physical listening equipment while achieving superior detection accuracy through computational analysis of multidimensional data.
2Productivity
If manual inference methods are used for leak detection, then the device complexity is low, but the productivity is reduced and labor intensity increases
Solution Approach 1:
The system performs automated self-diagnosis and leak detection without requiring manual intervention. The machine learning model automatically analyzes water usage data, identifies leak patterns, and provides detection results, eliminating the need for manual inference and significantly improving productivity while reducing labor intensity.
Solution Approach 2:
The patent replaces manual inference processes with automated machine learning algorithms that process water usage data automatically. This substitution dramatically increases detection efficiency and productivity while maintaining relatively low system complexity through the use of established machine learning techniques.
3Measurement precision
If simple water usage monitoring is implemented, then the device complexity is low, but the measurement precision and leak type identification capability are insufficient
Solution Approach 1:
The patent enhances measurement precision by collecting and analyzing multiple dimensions of data simultaneously: time-series water usage data, consumer information, and facility information. This multi-dimensional approach enables accurate leak type identification by analyzing patterns across different data dimensions rather than relying on simple single-parameter monitoring.
Solution Approach 2:
The system performs multiple functions using a unified machine learning framework: it detects water leaks, identifies leak types, and provides location information. This multi-functionality achieves high measurement precision for leak type identification without proportionally increasing device complexity, as a single system handles multiple detection tasks.
Data Source
AI summary
Disclosed are a device and a method for indoor water leak detection and type identification using multidimensional data that may receive water usage data and multidimensional data regarding water leak occurrence and perform use machine learning to analyze an indoor water leak period in detail, and may repeatedly perform an error verification procedure to determine whether actual water leaks are predicted by a learning model, thereby improving the accuracy of indoor water leak detection and type identification.