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Data preprocessing method

A data preprocessing and data technology, applied in the field of machine learning, can solve problems such as data redundancy and data loss, and achieve the effect of ensuring data, ensuring diversity, and avoiding data redundancy

Active Publication Date: 2020-06-16
浙江华网恒业科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

This technology helps gather cleaned data by analyzing multiple types of data streams from both Natural Disasters (TLD) and Man-Moving Disorders (MM). By doing this it saves storage space while still being able to identify any problematic areas based upon their attributes like location and severities. Additionally, if certain parts were found wrong during analysis they could be corrected before sending them back again into memory. Overall, these technical benefits help improve efficiency and reliability in predictive models used for various applications such as environmental monitoring systems.

Problems solved by technology

The technical problem addressed by this patented method for accurately identifying potential dangers caused by natural or human events like fires or explosions can include processing vast amounts of historical data from previous recordings with different types of errors that could affect future predictions made based on these new observations.

Method used

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Embodiment 1

[0031] Such as figure 1 As shown, this embodiment provides a data preprocessing method, which is used to process the collected data before the disaster risk prediction of the power towers, power transmission lines and power channels of the State Grid. The preprocessing provided by this embodiment The method is used to process the collected data before icing disaster or lightning disaster risk prediction, including the following steps:

[0032] Collection of historical information, including field maintenance department deployment history information, tower and line ledger history information, and meteorological history information. Among them, field maintenance department deployment history information includes line defect sub-information, hidden danger sub-information, and fault sub-information; meteorological historical information includes Weather condition sub-information, temperature sub-information, humidity sub-information, wind speed sub-information, wind direction sub...

Embodiment 2

[0043] Such as figure 2 As shown, this embodiment provides a data preprocessing method, which is used to process the collected data before the disaster risk prediction of the power towers, power transmission lines and power transmission channels of the State Grid. The preprocessing provided by this embodiment The processing method is used to process the collected data before the treeline discharge disaster, geological disaster or mechanical failure risk prediction, including the following steps:

[0044] Collection of historical information, including field maintenance department deployment history information, tower and line ledger history information, and meteorological history information. Among them, field maintenance department deployment history information includes line defect sub-information, hidden danger sub-information, and fault sub-information; meteorological historical information includes Weather condition sub-information, temperature sub-information, humidity ...

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Abstract

The invention discloses a data preprocessing method and relates to the field of machine learning, in particular to a method for processing acquired data before disaster risk prediction is performed ona power tower, a power transmission line and a power channel of a state grid, which comprises the following steps of: acquiring historical information, forming complete historical data, resampling the complete historical data, or selecting data characteristics to obtain a training set. The acquired data is processed, so that the data meets the prediction requirement.

Description

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Claims

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Application Information

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Owner 浙江华网恒业科技有限公司
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