Electric power communication network equipment reliability evaluation method based on capsule network

Abstract

The invention discloses an electric power communication network equipment reliability evaluation method based on a capsule network. The method comprises the following steps: carrying out reliability evaluation on equipment through a trained electric power communication network equipment reliability evaluation model, wherein the model construction process comprises the following steps of processingthe text information of the operation and maintenance data through a word embedding method to obtain an input word vector; carrying out feature extraction on the input word vector by using convolution filters with different sizes, combining the extracted features in a serial connection manner, and further carrying out convolution filtering on each position where the features are obtained by usingthe convolution filter to form a capsule feature vector; binding an activation value to each capsule feature vector; compressing the number of capsules, and then obtaining a new capsule feature vector and a new activation value through a capsule routing method based on kernel density estimation, wherein the new activation value is used for regression prediction; and inputting the new capsule feature vector into a decoder to reconstruct an input feature. The method can conveniently and accurately evaluate the reliability of the power communication network equipment.

Description

technical field [0001] The invention relates to the fields of equipment reliability evaluation and machine learning, in particular to a capsule network-based power communication network equipment reliability evaluation method. Background technique [0002] Modern power communication networks are rapidly increasing in size and complexity. As an important infrastructure of the modern smart grid, the power communication network not only provides various guarantee services required by the grid, but also monitors the equipment and sensors on the network in real time and maintains key operating data. Therefore, it is more and more important to evaluate and analyze the reliability of the equipment of the communication network itself. Traditional power communication network operation and maintenance usually means periodic maintenance or short-term fault alarm recovery, and automatic reliability assessment and prediction methods can further reduce the risk of serious network failure...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the deficiencies in the prior art, the present invention provides a method for evaluating the reliability of power communication network equipment based on capsule network, which solves the problem that the current prediction method relies on manual preprocessing and feature extraction, and cannot conveniently and accurately evaluate the power communication network Equipment Reliability Issues

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