Design and Application of Smart Vision Sensor Using Embedded Technology in Cost Management of Power Transmission and Transformation Project in Ningxia Companies

Abstract

The purpose is to solve the problems of cumbersome calculation, low accuracy, poor timeliness, rigid data acquisition, high cost, large volume, and insufficient signal processing capacity of traditional vision sensor (VS) in Ningxia companies. Firstly, this paper designs an embedded smart VS based on advanced RISC machines (ARM) processor. Secondly, it proposes a cost estimation algorithm for power transmission and transformation project (PTTP) based on particle swarm optimization-least squares support vector regression (PSO–LSSVR). Afterward, a cost estimation model of PTTP based on building information modeling (BIM) is proposed. Thirdly, historical cost data of PTTP of a Ningxia company within five years are selected as data samples to verify the accuracy of the PSO-LSSVR estimation algorithm and BIM model. The results show the following: (I) The measurement error of the designed smart VS is less than 4%, with high accuracy, which is suitable for large-scale measurement in the construction site. (II) The error of the PSO-LSSVR algorithm in engineering cost prediction is less than 20%, and the accuracy is higher than that of traditional support vector machine (SVM) and LSSVR algorithms. The optimization effect is remarkable and can be used for the feasibility analysis of PTTPs. (III) The proposed BIM-based PTTP cost estimation model error in the project cost estimation is controlled within 10%. With high accuracy, it can be applied to the PTTP management of Ningxia company. The purpose is to provide important technical support for the upgradation of traditional VS technology and the realization of visual management and rapid cost estimation of PTTP of Ningxia companies.