Water supply pipe network sensor arrangement optimization method based on multiple particle swarm optimization algorithm

Abstract

The invention discloses a water supply pipe network sensor arrangement optimization method based on a multiple particle swarm optimization algorithm. The method includes: a pipe network topology structure of a water supply pipe network is established, the complexity of each pipe network node in the water supply pipe network is obtained, hydraulic simulation and water quality simulation of the pipe network topology structure are performed, and the accessibility and the pollutant concentration of each pipe network node are obtained; population initialization of the multiple particle swarm optimization algorithm is performed at a main calculating node, and global search is conducted in the MAP stage; local search is conducted in the Reduce stage, and the newest global optimal individual is obtained; whether the fitness of the newest global optimal individual satisfies a preset convergence condition is determined, and if the fitness does not satisfy the preset convergence condition, iteration evolution is continued via moving to the task distribution step. According to the method, the technical problem of long optimization time of water supply pipe network sensor arrangement in the prior art is effectively solved, the monitoring effect is maximized (such as detecting pollution events with the fastest time), and safety risks due to pollution of drinking water are prevented.

Description

technical field [0001] The invention relates to the technical field of environmental engineering, in particular to a method for optimizing the arrangement of sensors in a water supply pipe network based on a multi-particle swarm optimization algorithm. Background technique [0002] The sensor layout of water supply network is a large-scale optimization problem. Since the urban water supply network consists of thousands of pipes, nodes, water valves, water pumps, etc., the scale is very large. Taking an urban pipe network with a scale of 10,000 nodes as an example, assuming that the sampling time of the water quality sensor is 10 minutes and the entire simulation period is 72 hours, the number of pollution events we need to simulate is 10,000Х72Х60 / 10. If the data for storing a pollution event takes up 4 bytes, it takes 172.8GB of computer memory. Considering the current simulator and single-node computer hardware, where each pollution event simulation takes 4 seconds, it wi...

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Problems solved by technology

[0004] The embodiment of the present invention solves the technical problem in the prior art that the sensor arrangement optimization time of the water supply n

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