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
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
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...
PUM
Abstract
Description
Claims
Application Information
- R&D Engineer
- R&D Manager
- IP Professional
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com