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A Complementary Method for Continuity Missing Data of Dam Deformation Monitoring

A deformation monitoring and missing data technology, applied in data mining, electrical digital data processing, digital data information retrieval, etc.

Active Publication Date: 2019-05-28
HOHAI UNIV
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  • Claims
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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 Complementary Method for Continuity Missing Data of Dam Deformation Monitoring
  • A Complementary Method for Continuity Missing Data of Dam Deformation Monitoring
  • A Complementary Method for Continuity Missing Data of Dam Deformation Monitoring

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[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 complementing continuity missing data in dam deformation monitoring. The method solves the problem of completing continuity missing data in dam deformation monitoring data. Firstly, the lack of continuity in the deformation monitoring data is preprocessed; secondly, from the perspective of global space, global time, local space, local time and semantics, the spatial-temporal similarity and functional Global and local space, time and semantic interpolation are performed on the missing data in the data; finally, a deep neural network model is constructed, and the preliminary results of the above-mentioned deformation monitoring missing data complement are used as input, and the deep neural network is used to realize non-linear fusion. Completion of continuous missing data in dam deformation monitoring.

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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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/215G06F11/07
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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