Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

A Classification Method of Stress Data of Tie Rods in Quay Bridge Based on K-Nearest Neighbor Algorithm

A data classification, nearest neighbor technology, applied in computing, computer components, instruments, etc., can solve problems such as not meeting engineering needs well, and achieve the effect of high precision, mature theory, easy to understand and implement

Active Publication Date: 2021-09-28
SHANGHAI MARITIME UNIVERSITY
View PDF8 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The manual detection method currently used does not need to spend a lot of manpower and material resources, and requires specialized and experienced engineers to carry out the work. However, the actual results still cannot well meet the actual engineering needs.

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
  • A Classification Method of Stress Data of Tie Rods in Quay Bridge Based on K-Nearest Neighbor Algorithm
  • A Classification Method of Stress Data of Tie Rods in Quay Bridge Based on K-Nearest Neighbor Algorithm
  • A Classification Method of Stress Data of Tie Rods in Quay Bridge Based on K-Nearest Neighbor Algorithm

Examples

Experimental program
Comparison scheme
Effect test

example

[0047] This paper selects the strain data of four weeks from the end of 2009 to the beginning of 2010 measured at the cross-section of the tie rod 2 meters away from the hinge in the quay bridge as a sample for classification. One data is collected every 10 seconds or so, and about 7000-8000 data can be collected in one day. Write the K-nearest neighbor algorithm with MATLAB software for data classification and analysis and drawing. The three training sets traindata1, traindata2, and traindata3 are set, and the classification categories are light load, medium load, and heavy load. See Table 1 below for the stress level. The samples in the training set correspond to several random points in the category. For the accuracy of the algorithm application, K=1 is selected, and the Euclidean distance between the test sample and the training sample is calculated. The test data belongs to the most recent category of training samples, and the classification is completed. .

[0048] Tab...

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

A method for classifying stress data of tie rods in quay cranes based on the K-nearest neighbor algorithm. The stress value data is collected through the strain sensor on the cross-section of the tie rods in the quay bridge 2 meters away from the hinge, and then the data is classified using the K-nearest neighbor algorithm. Statistical analysis of the load state on the cross-section of the tie rod 2 meters away from the hinge in the bridge is carried out to analyze the stress of the metal structure of the tie rod under different operations of the quay bridge, so as to draw a conclusion. Obtaining the working status of the quay crane from the massive engineering actual stress value data is conducive to the analysis and control of the situation in the actual use of the quay crane, and it is convenient to prolong the service life of the quay crane. The method based on the K-nearest neighbor algorithm in the present invention is simple and easy to use, easy to understand, high in precision, mature in theory, and has a better classification effect on quayside bridge data.

Description

technical field [0001] The invention belongs to the field of data classification of quay cranes, in particular to a method for classifying stress data of tension rods in quay cranes based on the K-nearest neighbor algorithm. Background technique [0002] As an important loading and unloading equipment in container ports, quay cranes are widely used in container port terminals. Whether the quayside crane can work normally directly affects the production efficiency and economic benefits of the port. Therefore, the data mining and state recognition of the mechanical characteristic information of the quay crane has become the mainstream research direction. By installing sensors on the key parts of the quay crane, a large amount of data information is collected. These data are collected during the operation of the quay crane and reflect the The mechanical characteristics of the bridge under various working conditions, because the stress of the metal structure of the quay bridge ...

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 Patents(China)
IPC IPC(8): G06K9/62
CPCG06F18/24147
Inventor 唐刚沈佳莉胡雄顾邦平
Owner SHANGHAI MARITIME UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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