Construction Technology and Quality Control of Power and Electrical Engineering Based on Convolutional Neural Network

Lei Xiao,Jian Su

doi:10.1155/2021/8964532

Abstract

In the context of the Internet era, more and more parties have begun to store, process, and analyze data, but the accompanying question is whether people are reasonable about the data under the impact of massive data, effective and efficient analysis, especially the problems faced in this project. This article aims to study the quality control problems faced by electric power and electrical engineering in the construction process through the use of convolutional neural networks. Under this idea, this article proposes a multilayer convolution method. The experimental results show that the use of the improved multilayer convolution method for the convolution method of the convolutional neural network can effectively improve the multiple analysis problems of small datasets in the construction of electric power and electrical engineering; in this way, the relevant data are analyzed; by controlling the quality of construction, the quality problem has been greatly improved. After comparison, it is concluded that the overall construction quality has increased by 35%.

Highlights

Living in the Internet age, there are all kinds of data around me every day. e data age advocates higher requirements for processing large amounts of data
Convolutional neural network is a deep learning network model based on biological perception information, which can greatly reduce the parameters of the deep network model and reduce its complexity
It uses forward propagation to calculate output and backpropagation to adjust weights and thresholds. e processing stack is stacked. e main advantage is that each neuron in the convolutional layer is locally connected to the feature map of the upper layer

Summary

Introduction

Living in the Internet age, there are all kinds of data around me every day. e data age advocates higher requirements for processing large amounts of data. Erefore, they proposed an automatic segmentation method based on convolutional neural network (CNN) to explore small 3 × 3 kernels [2]. It can be seen from this film that the convolutional neural network has more important application feasibility in brain tumors. Moeskops PMR believes that automatic segmentation in brain images is very important for quantitative analysis in large-scale studies of images obtained at all ages He proposed a method to automatically segment MR brain images into multiple tissue categories using convolutional neural networks. Chen Y proposed a regularized deep feature extraction (FE) method using convolutional neural network (CNN) for hyperspectral image (HSI) classification. E novelty of this article is shown in the following. (1) e use of the network is the same as the improved multilayer convolution method. e small dataset is researched and analyzed many times, and the construction data are professionally analyzed and used to ensure that the construction process can effectively guarantee the construction QC. (2) In the current process of power and electrical engineering construction, it is difficult to process and analyze engineering data, and it is difficult to guarantee the quality control of engineering construction

Construction Project Quality Management Objectives

Power and Electrical Engineering Experiment

Acceptance of Civil Works and Retest of Foundation

Quality Control Problems in the Construction Process

Findings

Notch filter 2 Reactive power