Water demand calibration method for water supply network nodes based on prior information of water demand

Abstract

The invention discloses a node water demand checking method based on the prior information of the water demand, which belongs to the checking method of the node water demand of a water supply pipe network. Firstly, the a priori water demand and its covariance matrix are estimated based on information such as water meters transmitted remotely from users, water bills, and user distribution; the monitoring data of pipe network pressure and flow and their covariance matrix are obtained through pipe network pressure and flow sensors; Kernel the objective function, and linearize the objective function, obtain the iterative step size of the node water demand, and update the node water demand; after each iteration, calculate the gradient vector of the objective function to the water demand, and stop when the modulus of the gradient vector is less than 1 iterate. This method introduces the prior information of the water demand of the water supply network nodes, thereby reducing the search space of the node algorithm, improving the inversion accuracy of the node water demand, and providing a scientific basis for the hydraulic modeling of the water supply network.

Description

technical field [0001] The invention belongs to a method for checking water demand of urban water supply pipe network nodes, in particular to a water supply pipe network node water demand check method based on prior information of water demand. Background technique [0002] The water supply pipe network model must be calibrated before it is put into practical use. The purpose is to make the calculated results of the pipe network model consistent with the data monitored by the sensors arranged in the pipe network. Model checking is to adjust the model parameters to make the model calculation results consistent with the sensor monitoring results. Pipe roughness and node flow are the main parameters affecting the accuracy of pipe network simulation. In general, the range of the roughness of the pipeline is not large, and the change with time is very stable, so it does not need to be checked frequently. However, the node traffic varies greatly with time and space. Even after a...

AI Technical Summary

Problems solved by technology

In addition, the greater the number of nodes, the greater the complexity and the greater the workload, which often takes several months to complete, requiring a lot of manpower and material resources

Therefore, the problem of water demand calibration has always been the bottleneck of hydraulic modeling of pipe network

However, the current value-based node water demand calibration algorithm generally only considers the monitoring information provided by the node pressure and pipeline flow sensors, and the inversion results tend to deviate from the real results.

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