Work Progress Management Optimization Of Municipal Engineering Based On Computational Intelligence

People’s life is more and more changed by municipal engineering. Computer aided technology is general method’s carrier and tool of project construction of municipal industry at home and abroad. The managers of municipal engineering want to provide general approach that convenience and can be in accordance with the development of municipal engineering enterprises and industry in the situation of different internal and external circumstances by continuing optimize data application technology and management level of engineering process.A variety of data including:geodesy, process management, actual consumption, caphal verification, complaint feedback are needed to be deal with every day municipal engineers. How to fast and effective extraction, conversion, mining data, change data into information and provide guidance for project management, has become practical requirements of the contemporary municipal engineers.The optimization and adjustment of process control is the important connotation of enterprise core competitiveness of contemporary municipal engineering industry. Explicit knowledge is the necessary conditions of the optimization and adjustment, which is the important way to ensure the process control’s quality and to sustain the project upgrade.Just based on above background, this article took some municipal comprehensive engineering (the author participated in) as object to do data batch clustering and mining processing adopting related statistical methods of computational intelligence(artificial intelligence, neural networks) applying general computing tools (self algorithms) to find the problem needed to be optimization in the process of project management.First of all. according to the project budget, the author did analysis to the data structure of the project. By analysis. this article divided the data into three categories:artificial, material, machinery. Meanwhile, the data was divided into query application and macroscopic type according to the actual situation of the project. With the reality needs of macroscopic scene, found the special conditions which data analysis and mining should have. To track the data flow in the project construction process by the projects organization chart, engineering data flow diagram and its analysis in the ministry of construction to find the links and constraints among the various departments, and then lay the foundation for the next optimization.Second, the author obtained the conclusion of data adjustment feasibility according to the subject characteristics, advantages of computational intelligence, and the actual:project’s technical and safety conditions. And the optimization approach in project process management was determined based on computational intelligence. The author used the data that listed in the municipal engineering project as the data collected samples, which react the characteristics of the engineering, realized the batch processing to large amounts of data in the process control. The actual data was classified according to the different representation, and remove the poor quality data that could have poor impact on process control in order to confirm the rationality of the optimization.Finally, the authors conducted feasibility studies to the number of training, the number of clusters and cluster stability in the application of process optimization which based en computational intelligence (artificial intelligence, neural networks), and draw relevant conclusions. At the same time, described the remaining issues in the course of the study.