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Water transportation pipe network leakage positioning method based on Bayesian decision theory and genetic algorithm

A genetic algorithm and decision-making theory technology, applied in the field of leakage location of water pipeline network, can solve problems such as low efficiency and high labor intensity, and achieve the effects of avoiding heavy work, high positioning efficiency, and ensuring the accuracy of positioning

Inactive Publication Date: 2014-02-05
GUANGDONG HLDG +2
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Problems solved by technology

[0003] The present invention aims to solve the problem of high labor intensity and low efficiency in the existing water pipeline network adopting the method of listening to location leakage, and proposes a water pipeline network leakage location method based on Bayesian decision theory and genetic algorithm

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  • Water transportation pipe network leakage positioning method based on Bayesian decision theory and genetic algorithm
  • Water transportation pipe network leakage positioning method based on Bayesian decision theory and genetic algorithm
  • Water transportation pipe network leakage positioning method based on Bayesian decision theory and genetic algorithm

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specific Embodiment approach 1

[0023] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 1. A method for locating leakage of water pipeline network based on Bayesian decision theory and genetic algorithm in the present invention is carried out according to the following steps:

[0024] 1. For the water pipeline network that needs to be located for leakage, determine the location of each pressure measurement point on GIS (Geographic Information System), set up pressure monitors on site, and establish PDD (Pressure Driven Node) according to DMA (Independent Metering Division). flow) leakage model;

[0025] 2. Collect water pressure signals for the water pipeline network with leakage;

[0026] 3. According to the Bayesian decision theory, the missing node number and the missing amount are used as independent variables, and the probability density of missing events is used as the dependent variable to establish a fitness function, namely:

[0027] Max : f ( ...

specific Embodiment approach 2

[0038] Embodiment 2. This embodiment is a further description of step 4 of Embodiment 1. The independent variable set of the genetic algorithm described in step 4 is: missing node number and missing amount; the dependent variable set is: missing event Probability Density.

specific Embodiment approach 3

[0039] Embodiment 3. This embodiment is a further description of step 4 of embodiment 1. The solution process of the genetic algorithm described in step 4 is as follows:

[0040] 1. The population is the number of the missing node and the amount of leakage, and the fitness evaluation standard is the probability density of the leakage event. Using MATLAB and EPANET hydraulic simulation software, the genetic algorithm and the simulation of pipeline network leakage based on the PDD model are used to locate the population in the leakage model. It is input into the PDD model as a parameter, and a leakage simulation process based on the PDD model will be performed for all individuals, and then the output pressure value will be returned to the leakage localization model, and then the fitness will be calculated. operate;

[0041] 2. After completing the evolutionary algebra, output the individuals whose probability is greater than the given value f' in the population, and obtain the p...

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Abstract

The invention provides a water transportation pipe network leakage positioning method based on a Bayesian decision theory and a genetic algorithm, and relates to the water transportation pipe network leakage positioning method based on the Bayesian decision theory and the genetic algorithm. The water transportation pipe network leakage positioning method aims at solving the problems that the existing water transportation pipe network adopts a sound listening leakage positioning method, the work intensity is high, and in addition, the efficiency is low. The water transportation pipe network leakage positioning method is realized through the following steps that a PDD leakage model is built according to DMA; a water transportation pipe network with leakage is subjected to water pressure signal collection; according to the Bayesian decision theory, the leakage node number and the leakage quantity are used as independent variables, and the probability density of leakage accidents is used as a dependent variable to build a target function; the genetic algorithm is utilized for solving the functional expressions, after the evolution algebra is completed, individuals with the probability greater than the given value f' in the population are output, and the possible leakage occurring position is obtained; according to calculation results, monitoring personnel are assigned to the possible leakage occurring position for checking or restoring pipelines. The water transportation pipe network leakage positioning method is applicable to the field of water transportation pipe network engineering.

Description

technical field [0001] The invention relates to a method for locating leakage of a water transmission pipeline network, in particular to a method for locating leakage of a water transmission pipeline network based on Bayesian decision theory and genetic algorithm. Background technique [0002] With the growth of population and the development of industry, the load of the water pipeline network has gradually increased in recent years. In addition, some pipe sections in the pipeline network are corroded and the operation of the pipeline network is unreasonable. As a result, leakage accidents often occur in the old pipeline network. The leakage not only seriously wastes water resources and energy, but also poses a hidden danger to drinking water safety. Therefore, establishing a fast and accurate leakage real-time location model is of great significance for saving water resources, ensuring the safety of urban drinking water supply and promoting social and economic development. ...

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Application Information

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IPC IPC(8): G06F19/00
Inventor 李冬平高金良李国斌乔怡超刁美玲叶健孙国胜张昭君王晶惠阮婷陈兵姜涛
Owner GUANGDONG HLDG
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