Abstract: The present invention relates to a method for acquiring the water leakage amount of a leakage area in a water distribution system. In the method, a hydraulic model is coupled with monitored data, and the water leakage amounts of different areas in the water distribution system are obtained through inversion by optimizing the spatial distribution of water leakage amounts. In addition to the coupling of the hydraulic model with the monitored data, virtual sectorization and pipe leakage risk assessment are used. By using a minimum difference between a simulated value and a monitored value at a pressure measurement point as a target, the water leakage amounts of different areas in the water distribution system are calculated by an optimization algorithm. Compared with the prior art, the present invention actualizes the effective identification of leakage areas in a water distribution system with a high leakage rate (i.e., with many leakage points).

Abstract: The present invention introduced a method for detecting pipe bursts in water distribution systems based on pressure disturbance extraction, including following steps: (1) collecting and pre-processing monitored pressure data, and establishing a matrix of monitored pressures; (2) analyzing time-domain and frequency-domain features of the matrix of monitored pressures by Fourier transform, extracting disturbances, and generating pressure disturbances matrices; (3) identifying outliers in the pressure disturbances matrices by isolation forest algorithm; and (4) further identifying detected outliers by calculating and qualitative index A and quantitative index B and outputting a result of pipe burst detection. Compared with previous methods, the pipe burst detection method introduced in the present invention is accurate and reliable, and is more applicable to large-scale complex water distribution systems.

Abstract: Provided are a composition, a cured polymer material formed from said composition, a method for forming a thermally conductive material on an article and an article having a thermally conductive material. Said thermally conductive material can be formed on an electronic device, and can be peeled off from said electronic device.

Abstract: The present invention relates to a method for acquiring the water leakage amount of a leakage area in a water distribution system. In the method, a hydraulic model is coupled with monitored data, and the water leakage amounts of different areas in the water distribution system are obtained through inversion by optimizing the spatial distribution of water leakage amounts. In addition to the coupling of the hydraulic model with the monitored data, virtual sectorization and pipe leakage risk assessment are used. By using a minimum difference between a simulated value and a monitored value at a pressure measurement point as a target, the water leakage amounts of different areas in the water distribution system are calculated by an optimization algorithm. Compared with the prior art, the present invention actualizes the effective identification of leakage areas in a water distribution system with a high leakage rate (i.e., with many leakage points).

Abstract: Provided are a composition, a cured polymer material formed from said composition, a method for forming a thermally conductive material on an article and an article having a thermally conductive material. Said thermally conductive material can be formed on an electronic device, and can be peeled off from said electronic device.

Abstract: The present invention relates to a method for tracking and locating a contamination source in a water distribution system based on consumer complaints, comprising following steps: S1: generating a contamination matrix by location information complained by consumers; S2: determining similarity between candidate nodes and classifying the candidate nodes; S3: adding a random complaint hysteresis time and constructing a consumer complaint sample; and S4: training, validating and testing a convolutional neural network by the consumer complaint sample, and using the convolutional neural network in practically tracking and locating a contamination source. Compared with the prior art, the present invention has the following advantages. The contamination source is located in the consumer complaint pattern, according to the real-time consumer complaints after a contamination accident occurs.

Abstract: The present invention introduced a method for detecting pipe bursts in water distribution systems based on pressure disturbance extraction, including following steps: (1) collecting and pre-processing monitored pressure data, and establishing a matrix of monitored pressures; (2) analyzing time-domain and frequency-domain features of the matrix of monitored pressures by Fourier transform, extracting disturbances, and generating pressure disturbances matrices; (3) identifying outliers in the pressure disturbances matrices by isolation forest algorithm; and (4) further identifying detected outliers by calculating and qualitative index A and quantitative index B and outputting a result of pipe burst detection. Compared with previous methods, the pipe burst detection method introduced in the present invention is accurate and reliable, and is more applicable to large-scale complex water distribution systems.