*Intelligent Data Analysis Project for Water Leakage Detection in Healthcare Settings

Project Background
A specific ward in a large general hospital long faced challenges with undetectable water pipe leaks and unpredictable leakage risks. Traditional manual inspections were not only inefficient but also prone to missing hidden leaks during nighttime and non-working hours. This not only wasted water resources but also posed potential risks such as facility damage and environmental safety hazards. To address this, a real-time and accurate leak detection solution using data analysis technology was needed to enable early identification and handling of potential risks.
Core Practical Roles of Data Analysis
1. Data Collection and Standardization: Laying a High-Quality Foundation for Analysis
The project captured real-time water flow data using high-precision sensing devices, generating 1 piece of raw data per second. Automated tools were then used to structure the data, including filling in missing values, calibrating abnormal fluctuations, and standardizing data formats—transforming unordered raw water flow data into “clean, analyzable data”. Additionally, based on the characteristics of water usage scenarios in the ward (e.g., differences in water usage patterns between working and non-working hours), the data was categorized and labeled by time period, pipe type, and other dimensions. This ensured that subsequent analysis could accurately align with actual on-site requirements.
2. Multi-Model Collaborative Analysis: Addressing the Core Pain Point of “Difficult Leak Identification”
Given the complexity of water usage patterns in healthcare settings (e.g., daily water use, equipment water use, and emergency water use), three customized data analysis models were developed to achieve “full-scenario coverage and high-precision identification”:
Real-Time Probability Analysis Model: By comparing real-time water flow data with a preset “normal water usage pattern library” (trained using historical normal data), it outputs a leak probability value. This quickly filters out abnormal data that significantly deviates from the normal range, providing an initial basis for real-time monitoring.
Long-Term Trend Analysis Model: Analyzing water flow trends during non-working hours (e.g., 2:00–5:00 AM) on a monthly cycle, it establishes dynamic baseline values. This enables accurate identification of hidden leaks characterized by long-term, low-flow “slow seepage”—a type of leak that is extremely difficult to detect with traditional methods due to minimal flow fluctuations, but can be effectively captured through long-term trend comparison.
Daily Baseline Comparison Model: Using water flow data from the previous day’s non-working hours as the “daily baseline”, it real-time compares the maximum flow within 5 minutes and the average flow within 15 minutes of the same time period on the current day. This quickly identifies “sudden low-flow leaks” (e.g., a loosely closed faucet), solving the problem of “difficulty in detecting small leaks and resulting waste before discovery”.
3. Anomaly Early Warning and Decision Support: Enabling Data to “Speak” and Drive Action
Automated early warning rules were set up using data analysis tools: when any model detects an abnormal water flow pattern (e.g., the probability model output exceeds a threshold, the trend model identifies a deviation from the baseline, or the baseline model captures abnormal flow), the system automatically triggers an early warning process. Combined with historical data, it generates a “preliminary judgment of the anomaly cause” (e.g., “high probability of continuous leakage” or “possible short-term equipment water use anomaly”), providing decision support for the operation and maintenance team and avoiding blind inspection.