Method for prejudging yarn dyeing performance, electronic device and storage medium

An electronic device, comprising:

• at least one processing unit;
• and
• a storage unit in signal communication with the at least one processing unit;
• wherein the storage unit stores an instruction executable by the at least one processing unit, to enable the at least one processing unit to execute:
• determining a yarn sample normally dyed in a batch for a yarn to be judged;
• performing spectral detection on the yarn sample normally dyed in the batch to obtain first spectral information [xi, yi], wherein xi is a wave number sampling value, yi is a spectral information intensity, and i is a natural number from 1 to 400;
• calculating a covariance by simulation through a Gaussian process kernel (RBF Kernel) of a following calculation formula I based on the first spectral information:
• k
• =
• σ
• 2
• ⁢
• exp
• ⁢
• (
• -
• 
• t
• m
• -
• t
• n
• 
• 2
• 2
• ⁢
• l
• 2
• )
• (
• I
• )
• wherein σ is 0.5, 1 is 1.0, tm corresponds to a value of yi in a case of i=m, tn corresponds to the value of yi in a case of i=n, and m and n are natural numbers from 1 to 400 respectively;
• obtaining a plurality of pieces of continuous second spectral information [xj, yj] for the yarn sample in the batch to be judged with a wave number sampling step, and establishing a Gaussian process regression model according to the covariance, wherein the plurality of pieces of continuous second spectral information [xj, yj] is a 2×N1 matrix, wherein j=1, 2, . . . , N1, and Ni is a sampling number of the yarn sample in the batch to be judged and is a natural number greater than or equal to 200;
• obtaining third spectral information [aj, bj] for the yarn sample to be detected, wherein the third spectral information [aj, bj] is a 2×N2 matrix, N2 is a sampling number of the yarn sample to be detected, and N2 is equal to N1;
• performing a subtraction operation on the 2×N2 matrix of the third spectral information [aj, bj] and the 2×N1 matrix of the plurality of pieces of continuous second spectral information obtained by establishing the Gaussian process regression model, to obtain a 2×N3 matrix [uj, vj], wherein N3 is equal to N1 and N2;
• and
• judging dyeing performance of the yarn sample to be detected according to a value of vj.

A non-transitory computer-readable storage medium storing a computer instruction thereon, wherein the computer instruction is non-transitory and is used to cause a computer to execute:

• determining a yarn sample normally dyed in a batch for a yarn to be judged;
• performing spectral detection on the yarn sample normally dyed in the batch to obtain first spectral information [xi, yi], wherein xi is a wave number sampling value, yi is a spectral information intensity, and i is a natural number from 1 to 400;
• calculating a covariance by simulation through a Gaussian process kernel (RBF Kernel) of a following calculation formula I based on the first spectral information:
• k
• =
• σ
• 2
• ⁢
• exp
• ⁢
• (
• -
• 
• t
• m
• -
• t
• n
• 
• 2
• 2
• ⁢
• l
• 2
• )
• (
• I
• )
• wherein σ is 0.5, 1 is 1.0, tm corresponds to a value of yi in a case of i=m, tn corresponds to the value of yi in a case of i=n, and m and n are natural numbers from 1 to 400 respectively;
• obtaining a plurality of pieces of continuous second spectral information [xj, yj] for the yarn sample in the batch to be judged with a wave number sampling step, and establishing a Gaussian process regression model according to the covariance, wherein the plurality of pieces of continuous second spectral information [xj, yj] is a 2×N1 matrix, wherein j=1, 2, . . . , N1, and N1 is a sampling number of the yarn sample in the batch to be judged and is a natural number greater than or equal to 200;
• obtaining third spectral information [aj, bj] for the yarn sample to be detected, wherein the third spectral information [aj, bj] is a 2×N2 matrix, N2 is a sampling number of the yarn sample to be detected, and N2 is equal to N1;
• performing a subtraction operation on the 2×N2 matrix of the third spectral information [aj, bj] and the 2×N1 matrix of the plurality of pieces of continuous second spectral information obtained by establishing the Gaussian process regression model, to obtain a 2×N3 matrix [uj, vj], wherein N3 is equal to N1 and N2;
• and
• judging dyeing performance of the yarn sample to be detected according to a value of vj.