Gas pipe network leakage grade judgment method based on improved evidence fusion algorithm

Abstract

The invention discloses a gas pipe network leakage grade judgment method based on an improved evidence fusion algorithm. The gas pipe network leakage grade judgment method comprises the steps that multiple evidence sources used for judging gas pipe network leakage are obtained through gas pipe network information measured by a sensor; and for weak decision evidences, the focal element weight of the focal element with the highest mass value is taken as 1, and the other focal elements are multiplied by the respective focal element weights respectively, so that the difference between the maximumtrust focal element and the other focal elements is effectively expanded, and the evidence fusion result is more reliable, and the convergence speed is high. For conflict evidences, average evidencesare adopted to replace the conflict evidences, and the conflict evidences are limited by the evidence distance weights, so that the influence of the conflict evidences on an evidence synthesis resultis reduced, and weak decision evidences can play a decision role. According to the invention, the accuracy of gas pipe network leakage level decision can be improved.

Description

technical field [0001] The invention relates to the technical field of decision-making evidence fusion, in particular to a gas pipeline network leakage level judgment method based on an improved evidence fusion algorithm. Background technique [0002] For the judgment of gas pipeline network leakage level, the method of evidence fusion can be adopted. This scheme uses the leakage information extracted by various sensors such as pressure and flow on the gas pipeline to construct evidence sources; these evidence sources are redundant, complementary and conflicting, and need to be effectively fused to improve the quality of the gas pipeline network. Leak diagnostic accuracy. [0003] The fusion of evidence sources can be realized by using Dempster-Shafer (DS) evidence theory for uncertainty reasoning. Evidence theory focuses on using Dempster synthesis rules to fuse multi-source information and provide decision support. Conflicting evidence is common in practical application...

AI Technical Summary

Problems solved by technology

[0004] (1) When the evidence is completely conflicting, the denominator of the composition rule in the algorithm is O, and the DS composition formula cannot be used

[0005] (2) When the evidence is highly conflicting, the result may be inconsistent with the intuition

Yager believes that all conflicting evidence is unavailable, so the normalization factor in the DS combination rule is removed, and all conflicting probabilities are assigned to uncertain propositions. However, since all evidence conflicts are negated, the uncertainty of the composite result is increased. ; On the contrary, Smets believes that all conflicts are available, and the conflict evidence is preserved through the set theory, but the high reliability is gathered in the union focal element, which is not good for decision-making; Sun Quan et al. think that the global conflict is partially available, using evidence Credibility redistributes conflicts; Dubois et al. no longer study the problem of global conflicts, but consider how to solve the problem of local conflicts, assign local conflicts to the intersection and union of conflict focal elements, and fuse them in a certain proportion to reduce unreliable evidence Influence on Fusion Results

Although the algorithm improvement method from the perspective of local conflicts is simple and effective, the new combination rules often destroy the good characteristics of DS combination rules, and the combination results are usually affected by the order of evidence combination

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