Method for detecting load states of bridge metal structure based on K-mean clustering algorithm

A metal structure and clustering algorithm technology, applied in computing, computer parts, instruments, etc., can solve immature problems and achieve effective clustering effects, simple and practical methods, and simple and effective methods

Inactive Publication Date: 2018-06-29
SHANGHAI MARITIME UNIVERSITY
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Problems solved by technology

However, the research on the monitoring method of the load state of the metal structure of the quay bridge is not yet mature.

Method used

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  • Method for detecting load states of bridge metal structure based on K-mean clustering algorithm
  • Method for detecting load states of bridge metal structure based on K-mean clustering algorithm
  • Method for detecting load states of bridge metal structure based on K-mean clustering algorithm

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Embodiment Construction

[0040] The present invention also provides a method for detecting the load state of the metal structure of the quay crane based on the K-means clustering algorithm. In order to make the present invention more obvious and easy to understand, the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0041] Such as figure 1 As shown, the process of the load state detection method of a shore crane metal structure based on the K-means clustering algorithm provided by the present invention includes:

[0042] Step 1. Establish a data monitoring system:

[0043] In order to effectively monitor the loading state of the metal structure of the quay crane (such as a tie rod), a remote online monitoring and evaluation system for the quay crane is established, and sensors (such as SMUADAPT21 strain sensors) are installed at corresponding measuring points on the metal structure to monitor the data. The sensor takes data e...

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Abstract

The invention discloses a method for detecting load states of a bridge metal structure based on a K-mean clustering algorithm. The method comprises the following steps of T1, establishing a data monitoring system, and installing a sensor at the corresponding testing point of the metal structure to monitor data, so as to monitor the load state of the bridge metal structure; T2, establishing a physical model between the load of the bridge metal structure and the sensor; T3, according to the physical model between the load of the bridge metal structure and the sensor, adopting the K-mean clustering algorithm to analyze the load state of the bridge metal structure, namely S1, obtaining the data of the corresponding testing point; S2, pre-processing the data; S3, calculating to obtain a plurality of types and a clustering center, so as to analyze the data of each type, thereby analyzing the load state of a bridge pull rod. The method has the advantages that the load of the bridge metal structure is analyzed by the K-mean clustering algorithm; SPSS (statistical product and service solutions) software is used for clustering and analyzing, and an initial center iterative defining method isused for clustering; the clustering effect is effective, and the method is simple and practical, and is quick and convenient.

Description

technical field [0001] The invention relates to the field of logistics transportation and the field of machine learning, in particular to a method for detecting the load state of a metal structure of a quay crane based on a K-means clustering algorithm. Background technique [0002] With the growth of the global economy, the world's shipping industry has begun to recover strongly, the shipping capacity has increased rapidly, and the container transportation business has grown rapidly, resulting in tight container handling capacity worldwide, and the global demand for quayside cranes continues to grow rapidly. At present, domestic port construction is at its peak, and the demand for port machinery is increasing day by day. Due to the growth of international transport trade, the global demand for quay bridges is gradually increasing. The increase in transportation volume has led to the increasing size of ships, which has led to an increase in the demand for quay cranes, which...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06Q10/08
CPCG06Q10/083G06F18/23213
Inventor 唐刚李建霞胡雄
Owner SHANGHAI MARITIME UNIVERSITY
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