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A method for completing continuous missing data of dam deformation monitor

A deformation monitoring, missing data technology, applied in data mining, electrical digital data processing, special data processing applications, etc.

Active Publication Date: 2018-12-28
HOHAI UNIV
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  • Abstract
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Problems solved by technology

[0006] Purpose of the invention: Aiming at the missing and stable historical input of dam deformation monitoring continuity loss data and the complex nonlinear space-time relationship contained therein, the present invention discloses a method for complementing continuity loss of dam deformation monitoring. Complementary and heterogeneous information in the semantic view, using deep neural network to achieve nonlinear fusion, complete the missing data completion of dam deformation monitoring continuity

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  • A method for completing continuous missing data of dam deformation monitor
  • A method for completing continuous missing data of dam deformation monitor
  • A method for completing continuous missing data of dam deformation monitor

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Embodiment Construction

[0057] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0058] figure 1 Provides an overall framework diagram of the method for completing the missing data of dam deformation monitoring continuity. This method is divided into two parts, namely, the pre-estimation of the missing data of the dam deformation monitoring continuity and the fusion and completion of the missing data of the dam deformation monitoring continuity. . Through IDW, SES, UCF, MD-CF and SE respectively, the completion results of missing data are obtained, and the continuous missing ...

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Abstract

The invention discloses a method for completing continuous missing data of dam deformation monitoring, which solves the problem of completing continuous missing data of dam deformation monitoring. Firstly, the continuity loss of deformation monitoring data is pretreated. Secondly, from the angles of global space, global time, local space, local time and semantics, the spatio-temporal similarity and functional similarity of deformation monitoring points are calculated respectively, and the missing data in deformation monitoring data are interpolated globally and locally in space, time and semantics. Finally, a depth neural network model is constructed to complete the continuous missing data in dam deformation monitoring. The preliminary results of the missing data are used as input, and thenonlinear fusion is realized by using the depth neural network representation ability.

Description

technical field [0001] The present invention relates to a continuous data missing complement method, specifically a multi-view deep fusion-based missing data complement method for dam deformation monitoring, which captures complex nonlinear spatio-temporal relationships through a deep neural network to complete dam deformation monitoring The invention provides continuous missing data completion, which belongs to the technical field of data mining. Background technique [0002] A large number of sensors are deployed in the concrete dam, and they cooperate with each other to continuously monitor the real-time state of the dam deformation. The data generated by the sensor has spatio-temporal characteristics, but due to its own hardware, communication errors and severe wireless interference and other influencing factors, a large number of original sensor data are missing, and in extreme cases, continuous data is missing. The lack of these data not only affects real-time monitor...

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Application Information

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IPC IPC(8): G06F17/30G06F11/07
CPCG06F11/0793G06F2216/03
Inventor 毛莺池张建华高建陈豪平萍王龙宝
Owner HOHAI UNIV
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