METHOD AND APPARATUS FOR PROCESSING DATA SEQUENCE

1 . A method for processing a data sequence, the method comprising:

generating a Hankel matrix based on a to-be-processed data sequence, the to-be-processed data sequence comprising zigzag noise;

performing singular value decomposition on the Hankel matrix to obtain a left singular matrix, a singular value vector, and a right singular matrix, components of each dimension of the singular value vector being ordered from large to small;

determining a noise component in each component of the singular value vector;

zeroing each dimension of noise component in the singular value vector;

generating a reconstructed Hankel matrix based on the left singular matrix, the singular value vector after zeroing, and the right singular matrix; and

generating a processed data sequence based on the reconstructed Hankel matrix.

2 . The method according to claim 1 , wherein the generating a Hankel matrix based on a to-be-processed data sequence, comprises:

determining whether the to-be-processed data sequence comprises zigzag noise; and

generating, in response to determining that the to-be-processed data sequence comprises zigzag noise, the Hankel matrix based on the to-be-processed data sequence.

3 . The method according to claim 2 , wherein the to-be-processed data sequence comprises N data; and

the generating the Hankel matrix based on the to-be-processed data sequence, comprises:

determining, according to N, a number of rows R and a number of columns C of the Hankel matrix, wherein a sum of R and C is equal to a sum of N plus 1; and

setting the to-be-processed data sequence to be: X=[x 1 , x 2 , . . . , x N ], and calculating to obtain the Hankel matrix H according to a following formula:

H ( i,j )= x i+j−1

wherein, i is an integer between 1 and R, and j is an integer between 1 and C.

4 . The method according to claim 3 , wherein the generating a processed data sequence based on the reconstructed Hankel matrix, comprises:

setting the reconstructed Hankel matrix to be H′, and generating the processed data sequence X′=[x 1 ′, x 2 ′, . . . , x N ′] based on the reconstructed Hankel matrix H′ according to a following formula:

x

k

′

=

1

n

-

m

+

1



∑

j

=

m

n



H

′



(

k

-

j

+

1

,

j

)

,



m

=

max



(

1

,

k

-

R

+

1

)

,



n

=

min



(

C

,

k

)

wherein, k is an integer between 1 and N.

5 . The method according to claim 4 , wherein the determining a noise component in each component of the singular value vector, comprises:

setting a positive integer w to 1, the singular value vector being E={σ 1 , σ 2 , . . . , σ M }, wherein M is a positive integer;

performing a following noise component determining operation: calculating a noise suppression ratio ρ w corresponding to a component of a w th dimension of the singular value vector according to a following formula:

ρ

w

≈

∑

i

=

1

w



σ

i

∑

i

=

1

M



σ

i

determining, in response to determining that the noise suppression ratio ρ w obtained by calculation is greater than or equal to a preset noise suppression ratio threshold, components between the w th dimension and an M th dimension of the singular value vector as noise components, and ending the noise component determining operation, wherein the preset noise suppression ratio threshold is a value greater than 0 and less than 1; and updating, in response to determining that the noise suppression ratio ρ w obtained by calculation is not greater than or equal to the preset noise suppression ratio threshold, w to a sum of w plus 1, and continuing performing the noise component determining operation.

6 . The method according to claim 4 , wherein the determining a noise component in each component of the singular value vector, comprises:

setting the singular value vector to be E={σ 1 , σ 2 , . . . , σ M }, wherein M is a positive integer;

finding a noise boundary dimension v from the singular value vector, wherein a noise suppression ratio of a component of a v th dimension among noise suppression ratios of components of all dimensions of the singular value vector obtained by calculation calculated according to a following formula is closest to a preset noise suppression ratio threshold:

ρ

w

≈

∑

i

=

1

w



σ

i

∑

i

=

1

M



σ

i

wherein, w is an integer between 1 and M; and

determining components between the v th dimension and an M th dimension of the singular value vector as noise components.

7 . An apparatus for processing a data sequence, the apparatus comprising:

at least one processor; and

a memory storing instructions, the instructions when executed by the at least one processor, cause the at least one processor to perform operations, the operations comprising:

generating a Hankel matrix based on a to-be-processed data sequence, the to-be-processed data sequence comprising zigzag noise;

performing singular value decomposition on the Hankel matrix to obtain a left singular matrix, a singular value vector, and a right singular matrix, components of each dimension of the singular value vector being ordered from large to small;

determining a noise component in each component of the singular value vector;

zeroing each dimension of noise component in the singular value vector;

generating a reconstructed Hankel matrix based on the left singular matrix, the singular value vector after zeroing, and the right singular matrix; and

generating a processed data sequence based on the reconstructed Hankel matrix.

8 . The apparatus according to claim 7 , wherein the generating a Hankel matrix based on a to-be-processed data sequence, comprises:

determining whether the to-be-processed data sequence comprises zigzag noise; and

generating, in response to determining that the to-be-processed data sequence comprises zigzag noise, the Hankel matrix based on the to-be-processed data sequence.

9 . The apparatus according to claim 8 , wherein the to-be-processed data sequence comprises N data; and

the generating the Hankel matrix based on the to-be-processed data sequence, comprises:

determining, according to N, a number of rows R and a number of columns C of the Hankel matrix, wherein a sum of R and C is equal to a sum of N plus 1; and

setting the to-be-processed data sequence to be: X=[x 1 , x 2 , . . . , x N ], and calculate to obtain the Hankel matrix H according to a following formula:

H ( i,j )= x i+j−1

wherein, i is an integer between 1 and R, and j is an integer between 1 and C.

10 . The apparatus according to claim 9 , wherein the generating a processed data sequence based on the reconstructed Hankel matrix, comprises:

setting the reconstructed Hankel matrix to be H′, and generate the processed data sequence X′=[x 1 ′, x 2 ′, . . . , x N ′] based on the reconstructed Hankel matrix H′ according to a following formula:

x

k

′

=

1

n

-

m

+

1



∑

j

=

m

n



H

′



(

k

-

j

+

1

,

j

)

,



m

=

max



(

1

,

k

-

R

+

1

)

,



n

=

min



(

C

,

k

)

wherein, k is an integer between 1 and N.

11 . The apparatus according to claim 10 , wherein the determining a noise component in each component of the singular value vector, comprises:

setting a positive integer w to 1, the singular value vector being E={σ 1 , σ 2 , . . . , σ M }, wherein M is a positive integer;

performing a following noise component determining operation: calculating a noise suppression ratio ρ w corresponding to a component of a w th dimension of the singular value vector according to a following formula:

ρ

w

≈

∑

i

=

1

w



σ

i

∑

i

=

1

M



σ

i

determining, in response to determining that the noise suppression ratio ρ w obtained by calculation is greater than or equal to a preset noise suppression ratio threshold, components between the w th dimension and an M th dimension of the singular value vector as noise components, and ending the noise component determining operation, wherein the preset noise suppression ratio threshold is a value greater than 0 and less than 1; and updating, in response to determining that the noise suppression ratio ρ w obtained by calculation is not greater than or equal to the preset noise suppression ratio threshold, w to a sum of w plus 1, and continuing performing the noise component determining operation.

12 . The apparatus according to claim 10 , wherein the determining a noise component in each component of the singular value vector, comprises:

setting the singular value vector to be E={σ 1 , σ 2 , . . . , σ M }, wherein M is a positive integer;

finding a noise boundary dimension v from the singular value vector, wherein a noise suppression ratio of a component of a v th dimension among noise suppression ratios of components of all dimensions of the singular value vector obtained by calculation calculated according to a following formula is closest to a preset noise suppression ratio threshold:

ρ

w

≈

∑

i

=

1

w



σ

i

∑

i

=

1

M



σ

i

wherein, w is an integer between 1 and M; and

determining components between the v th dimension and an M th dimension of the singular value vector as noise components.

13 . A non-transitory computer readable storage medium, storing a computer program thereon, the computer program, when executed by a processor, causes the processor to perform operations, the operations comprising:

generating a Hankel matrix based on a to-be-processed data sequence, the to-be-processed data sequence comprising zigzag noise;

performing singular value decomposition on the Hankel matrix to obtain a left singular matrix, a singular value vector, and a right singular matrix, components of each dimension of the singular value vector being ordered from large to small;

determining a noise component in each component of the singular value vector;

zeroing each dimension of noise component in the singular value vector;

generating a reconstructed Hankel matrix based on the left singular matrix, the singular value vector after zeroing, and the right singular matrix; and

generating a processed data sequence based on the reconstructed Hankel matrix.