The current project cost system requires high data scale, small amount of data and large prediction deviation. In order to improve the prediction accuracy of the whole process cost of construction project, this paper designs a whole process project cost prediction system based on improved support vector machine. In the hardware part of the system, the control core adopts arm controller S3C6410 and introduces 4G communication module to analyze the actual engineering data with the support of hardware. In the software part, the whole process cost prediction index system of the construction project is established, the index is reduced by the principal component method, and the support vector machine is improved by particle swarm optimization algorithm to realize the whole process cost prediction of the project. The system function test results show that the average prediction deviation of the designed system is 4.11%, the average prediction deviation of the cost prediction system is 3.05%, and the average prediction deviation of the system is 1.57%.
The consistency of empirical comparisons of regression and analogy-based software project cost prediction
