1. A method for recognizing and classifying banknotes in a banknote processing device comprising: acquiring, by a processor, sample information of brand new banknotes to be learned and banknote sample information to be recognized, wherein the brand new banknotes are banknotes with brightness in a predetermined degree; establishing, by the processor according to a preset rule, a banknote sample signal degeneration model, wherein the banknote sample signal degeneration model comprises: a banknote condition degeneration model established based on linear change of image brightness, and a banknote image degeneration model established based on randomness of a statistic model, wherein the banknote image degeneration model comprises signal degeneration models for banknote contamination, banknote incompletion, banknote crack, and banknote fold or deflection, and the banknote condition degeneration model comprises degeneration models for banknotes in brand new condition, banknotes in 80%-90% new condition, banknotes in 70%-80% new condition, and banknotes in 0-70% new condition, wherein the establishing the banknote contamination degeneration model according to the preset rule comprises presetting that a banknote contamination region is circular and a stain is circular, and each banknote only have one contamination region; and determining, according to statistics analysis, that probability density curves for a position of the contamination region and a position of the stain in the contamination region are constants, i.e., the probability density curves are in uniform distribution X˜U(a,b), and a probability density curve for a size of the contamination region and probability density curves for size, density and gray value of the stain are in normal distribution X˜N(μ,σ 2 ); inputting, by the processor, the sample information into the banknote sample signal degeneration model to acquire various banknote sample information corresponding to the brand new banknotes to be learned, wherein the various banknote sample information corresponding to the brand new banknotes to be learned comprises sample information for banknotes with brightness in varying degrees; inputting, by the processor, the various banknote sample information to perform classifier learning, and outputting a banknote classification model; and performing, by the processor, sample signal preprocessing and feature extraction on the sample information to be recognized, performing classification decision on the banknote to be recognized by using the classification model, and outputting a final classification result.
1. A method for recognizing and classifying banknotes in a banknote processing device comprising: acquiring, by a processor, sample information of brand new banknotes to be learned and banknote sample information to be recognized, wherein the brand new banknotes are banknotes with brightness in a predetermined degree; establishing, by the processor according to a preset rule, a banknote sample signal degeneration model, wherein the banknote sample signal degeneration model comprises: a banknote condition degeneration model established based on linear change of image brightness, and a banknote image degeneration model established based on randomness of a statistic model, wherein the banknote image degeneration model comprises signal degeneration models for banknote contamination, banknote incompletion, banknote crack, and banknote fold or deflection, and the banknote condition degeneration model comprises degeneration models for banknotes in brand new condition, banknotes in 80%-90% new condition, banknotes in 70%-80% new condition, and banknotes in 0-70% new condition, wherein the establishing the banknote contamination degeneration model according to the preset rule comprises presetting that a banknote contamination region is circular and a stain is circular, and each banknote only have one contamination region; and determining, according to statistics analysis, that probability density curves for a position of the contamination region and a position of the stain in the contamination region are constants, i.e., the probability density curves are in uniform distribution X˜U(a,b), and a probability density curve for a size of the contamination region and probability density curves for size, density and gray value of the stain are in normal distribution X˜N(μ,σ 2 ); inputting, by the processor, the sample information into the banknote sample signal degeneration model to acquire various banknote sample information corresponding to the brand new banknotes to be learned, wherein the various banknote sample information corresponding to the brand new banknotes to be learned comprises sample information for banknotes with brightness in varying degrees; inputting, by the processor, the various banknote sample information to perform classifier learning, and outputting a banknote classification model; and performing, by the processor, sample signal preprocessing and feature extraction on the sample information to be recognized, performing classification decision on the banknote to be recognized by using the classification model, and outputting a final classification result. 5. The method according to claim 1 , wherein the establishing the banknote crack degeneration model according to the preset rule comprises: randomly acquiring a line segment s with a length of L on the boundary of the banknote, wherein L is in uniform distribution, Lε(0,MaxL) , and MaxL is a maximum length of the boundary of the banknote; determining a position of another point N, wherein a distance between the point N and a midpoint M of the line segment s is 1, and an angle between the line segment MN and the line segment s is, wherein lε(0,Maxl) , the angle αε(π/3,2π/3) , and α and 1 are in normal distribution; and determining a triangle region bounded by the point N and the segment line s as the crack region, and filling the crack region with background color.