The invention discloses a
system and a method for realizing
electric energy meter abnormity diagnosis by applying a
quantum particle swarm algorithm, and relates to the technical field of
electric energy metering. An architecture
system of
the Internet of Things is constructed, remote monitoring of abnormal data of an
electric energy meter is realized, change and extension of
data information of the electric energy meter are realized by using a
wavelet change method,
noise interference can be suppressed by using
wavelet transform, the
feature extraction precision is improved, the accuracy is high, and the performance is more stable. An improved
quantum particle swarm algorithm is used, particle swarms can be effectively screened; the convergence speed of the particle swarm is accelerated;according to the method, a BP neural network
algorithm model is utilized to prevent simultaneous falling into local extremum, a good effect is shown in the aspects of convergence rate and
global optimum searching, rapid diagnosis of abnormal data can be realized by utilizing the BP neural network
algorithm model, a large amount of electric energy meter
data information is rapidly calculated withina few seconds, and
data analysis and judgment are realized.