The Study On Urban Water Supply Network Simulation And Monitoring Model

With the increase of the social productivity, the continuous improvement of the population of living standards, the speed and scale of urbanization also becomes faster and faster, make the continuous increase of urban water consumption. The pipe network of city's water supply is an important part of the infrastructure. Not only the security of water supply pipe network directly affects the security of city water supply, but also affects the city's social and economic development. Therefore, the establishment and improvement of water supply network security and management system, real-time monitoring of urban water supply network and simulation, improving the modern enterprise management of urban water supply, reducing supply costs is the only way to increase economic efficiency. Exploring how to make a unified water supply system for monitoring, rational distribution, optimize and manage, to conserve energy and to achieve the purpose of ensuring safe water supply, water supply systems to improve the economic and social benefits are a great significance. This article is divided into three parts: water supply network inversion of the friction factor C, according to the network of the microscopic model to verify; three simulation ways'comparison among the actual pipe network; establishment the simulation model is based on SVM algorithm.1. Introduced the hybrid particle swarm optimization (HPSO) algorithm's the basic principles and realization. And then it gives based on HPSO algorithm inversion process of the friction factor, and the node pressure method calculates pressure values of each node.2. Based on the network of a real miniature 2.5m×2.5m simplify verifies the results of inversion of friction factor and the node water pressure's calculation ,and achieves the desire.3. Simulating to the actual pipe network of a regional macro regression model, first of all the basis of one day of each node in the network's based-data changes between time and flow. Individually introduce to based on least squares method, BP neural networks, support vector machine method simulate the basis data of the actual pipe network. The simulation methods based on support vector machine to simulate the operational status of the network better than others.