Optical network evaluation method

Abstract

The present invention discloses an optical network evaluation method including the steps of building an AHP-based network evaluation model based on a necessary evaluation factor, an factor and a guideline influencing a target and a comprehensive evaluation formula; completing a pairwise comparison matrix that A is equal to (aij) n x n, where aij is the ratio of influence of an i-th factor on top factors to influence of an j-th factor on top factors in an n-th layer; determining an eigenvector w and a maximum eigenvalue [lambda] of the pairwise comparison matrix; carrying out a consistency check, and proceeding to step five if the consistency check is passed, or returning to step two if the consistency check fails; calculating a combination weight vector w(s) according to the eigenvector w and carrying out a combination consistency check; and carrying out a comprehensive network evaluation according to the combination weight vector w(s). The optical network evaluation method has the characteristics of accuracy, flexibility and high adaptability.

Description

technical field [0001] The present invention relates to the communication field, in particular to an optical network evaluation method. Background technique [0002] As the basic network that provides communication services, the optical network is responsible for transmitting more than 80% of the circulating information in the communication network, and its quality plays a vital role in the communication services carried. Therefore, it is very necessary to evaluate and analyze various problems existing in the optical network, and put forward targeted suggestions for network construction and rectification. [0003] Some of the current evaluation methods only focus on one evaluation indicator, and the obtained evaluation results are often too one-sided to evaluate the network comprehensively. Another part of the evaluation method is aimed at multiple evaluation indicators, which is a comprehensive consideration of multiple indicators. Existing multi-index evaluation methods ...

AI Technical Summary

Problems solved by technology

However, when all factors are considered comprehensively, it is necessary to introduce weight parameters artificially. The introduction of weight parameters mostly depends on human experience. Especially in some methods, there are many factors considered. Simple and direct determination of weight parameters is often too subjective and lacks scientificity and accuracy. , which in turn leads to the problem of insufficient accuracy of the evaluation results

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