Engineering safety monitoring system area division method based on measuring point space-time characteristic

Abstract

The invention discloses an engineering safety monitoring system area division method based on a measuring point space-time characteristic. The method comprises two phases of measuring point space-time characteristic expression and area division. In the measuring point space-time characteristic expression phase, a time-sequence noise reduction automatic encoder is used to compress a high-dimensional monitoring data table and increase a measuring point time sequence characteristic, and measuring point spatial characteristics (coordinate position information, measuring point attributes and the like) are represented through a normalization method. In the area division phase, an area division method based on the measuring point space-time characteristic is adopted, and an auxiliary target variable optimization area is used to divide a target function so that an area division result reflects a physical law of operation of a structural body, and a global security evaluation problem is divided into a local single domain. In the invention, through analyzing a measuring point monitoring data change rule, comprehensive evaluation of a dam operating condition is realized and a decision-making basis is provided for engineering safety analysis.

Description

technical field [0001] The invention belongs to the field of engineering safety monitoring, in particular to an area division method of an engineering safety monitoring system based on the time-space characteristics of measuring points. Background technique [0002] The rapid development of sensors and Internet of Things technology has made the monitoring methods of large civil engineering structures more and more abundant. Taking the safety monitoring of a hydropower dam as an example, various sensors (measuring points) are deployed inside the dam, including strain, seepage pressure, displacement, steel bar gauge, inclinometer, dynamometer, etc., through wireless or Connected by optical fiber to form an automatic safety monitoring system. Sensors monitor a variety of physical quantities, such as displacement, deformation, opening and closing, pressure, stress, etc., and use professional models to analyze the monitoring data, perceive changes in physical quantities in vario...

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Problems solved by technology

Traditional clustering methods can handle low-dimensional feature data well, but when faced with high-dimensional data, there are problems such as long convergence time and low clustering accuracy.

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