Building construction safety monitoring method based on convolutional neural network

A convolutional neural network and safety monitoring technology, applied in the field of construction safety evaluation, can solve the problems of inability to monitor in real time, single evaluation factors, and high calculation costs

Active Publication Date: 2020-04-07
CHANGAN UNIV
View PDF4 Cites 13 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to overcome the shortcomings of single evaluation factors, high calculation cost and inability to monitor in real time in the existing construction safety monitoring method, and to provide a construction safety monitoring method based on convolutional neural network

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Building construction safety monitoring method based on convolutional neural network
  • Building construction safety monitoring method based on convolutional neural network
  • Building construction safety monitoring method based on convolutional neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0078] Taking a certain construction project as an example, the implementation process of the construction safety monitoring method based on CNN convolutional neural network according to the present invention is as follows, as figure 1 shown, including:

[0079] S1. Establish an evaluation index system

[0080] In this project, 30 different operators were surveyed and the sensors were arranged to measure the data of the operators; at the same time, the sensors were arranged to measure the data of the working environment; and the data of safety management measures were obtained through safety checklists and on-site investigations.

[0081] For the operator data, part of it is collected through safety questionnaires, including the operator’s age, weight, vision, hearing, work experience, education experience, economic status, and heart disease history; the other part is collected in real time through sensors, including the operator’s body temperature , heart rate, blood pressur...

Embodiment 2

[0107] As another example, this time, the safety evaluation data of randomly arranged operators, working environment, construction site safety management and safety measures in step S3 described in Example 1 are rearranged, and the random arrangement of evaluation indicators is no longer used but the The evaluation indicators with high importance are moved to the middle, and the evaluation indicators with low importance are moved to the surrounding matrix, because during the movement of the convolution kernel function, the number of data extractions in the surrounding borders is less, and the data except for the surrounding borders The number of extractions is high.

[0108] According to the model trained in Example 1, output several of the trained convolution kernel functions, observe the size of the convolution kernel function and select a larger value in the convolution kernel function, and extract its corresponding evaluation index. For Example 1, it is obtained that a val...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a building construction safety monitoring method based on a convolutional neural network. The building construction safety monitoring method based on the convolutional neural network comprises the following steps: 1) obtaining safety management measure data of operators, operation environments and building construction sites, obtaining a safety evaluation grade, and forminga training set and a test set of a CNN convolutional neural network model through preprocessing; 2) taking data of operators, operation environments and safety management measures in the training setas input, taking safety evaluation levels as output, determining a safety monitoring index arrangement mode and a CNN convolution neural network kernel function through training, and storing a trained CNN convolution neural network model; and 3) predicting the safety of the operator and the operation environment. The building construction safety monitoring method overcomes the defects that an existing monitoring method is inaccurate in problem evaluation result, and the evaluation factor of the evaluation method is single, and the calculation cost is high, and real-time monitoring cannot be achieved.

Description

technical field [0001] The invention belongs to the field of building construction safety evaluation, and in particular relates to a building construction safety monitoring method based on a convolutional neural network. Background technique [0002] With the advancement of production technology and in order to meet the needs of life, the construction industry has developed rapidly, and the number of employees in the construction industry has increased year by year in recent years. But at the same time, construction safety accidents are also the types of accidents with a high fatality rate. A construction safety monitoring method is proposed, which is of great significance to the prevention of safety accidents. [0003] Nowadays, there are many problems in the construction safety monitoring and evaluation methods: First, the monitoring and evaluation indicators are too single, such as the man-to-man stand-by method, the placement of monitoring cameras, and the simple sensor...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08G06Q10/06G06Q50/08
CPCG06N3/08G06Q10/0635G06Q50/08G06N3/045
Inventor 赵瑞峰王凯翟越汪铁楠杜菁
Owner CHANGAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap