您可以使用scipy.linalg.orthogonal_procrustes。下面是一个演示。请注意，函数generateAB的存在只是为了生成演示中的数组A和B。计算的关键步骤是将A和B居中，然后调用orthogonal_procrustes。

import numpy as np
from scipy.stats import ortho_group
from scipy.linalg import orthogonal_procrustes

def generateAB(shape, noise=0, rng=None):
    # Generate  A and B for the example.
    if rng is None:
        rng = np.random.default_rng()
    m, n = shape

    # Random matrix A
    A = 3 + 2*rng.random(shape)
    Am = A.mean(axis=0, keepdims=True)

    # Random orthogonal matrix T
    T = ortho_group.rvs(n, random_state=rng)

    # Target matrix B
    B = ((A - Am) @ T + rng.normal(scale=noise, size=A.shape)
         + 3*rng.random((1, n)))

    # Include T in the return, but in a real problem, T would not be known.
    return A, B, T

# For reproducibility, use a seeded RNG.

rng = np.random.default_rng(0x1ce1cebab1e)

A, B, T = generateAB((7, 5), noise=0.01, rng=rng)

# Find Q.  Note that `orthogonal_procrustes` does not include

# dilation or translation.  To handle translation, we center

# A and B by subtracting the means of the points.

A0 = A - A.mean(axis=0, keepdims=True)
B0 = B - B.mean(axis=0, keepdims=True)

Q, scale = orthogonal_procrustes(A0, B0)

with np.printoptions(precision=3, suppress=True):
    print('T (used to generate B from A):')
    print(T)
    print('Q (computed by orthogonal_procrustes):')
    print(Q)
    print('\nCompare A0 @ Q with B0.')
    print('A0 @ Q:')
    print(A0 @ Q)
    print('B0 (should be close to A0 @ Q if the noise parameter was small):')
    print(B0)

输出量：

T (used to generate B from A):
[[-0.873  0.017  0.202 -0.44  -0.054]
 [-0.129  0.606 -0.763 -0.047 -0.18 ]
 [ 0.055 -0.708 -0.567 -0.408  0.088]
 [ 0.024  0.24  -0.028 -0.168  0.955]
 [ 0.466  0.272  0.235 -0.78  -0.21 ]]
Q (computed by orthogonal_procrustes):
[[-0.871  0.022  0.203 -0.443 -0.052]
 [-0.129  0.604 -0.765 -0.046 -0.178]
 [ 0.053 -0.709 -0.565 -0.409  0.087]
 [ 0.027  0.239 -0.029 -0.166  0.956]
 [ 0.47   0.273  0.233 -0.779 -0.21 ]]

Compare A0 @ Q with B0.
A0 @ Q:
[[-0.622  0.224  0.946  1.038  0.578]
 [ 0.263  0.143 -0.031 -0.949  0.492]
 [-0.49   0.758  0.473 -0.221 -0.755]
 [ 0.205 -0.74   0.065 -0.192 -0.551]
 [-0.295 -0.434 -1.103  0.444  0.547]
 [ 0.585 -0.378 -0.645 -0.233  0.651]
 [ 0.354  0.427  0.296  0.113 -0.963]]
B0 (should be close to A0 @ Q if the noise parameter was small):
[[-0.627  0.226  0.949  1.032  0.576]
 [ 0.268  0.135 -0.028 -0.95   0.492]
 [-0.493  0.765  0.475 -0.201 -0.75 ]
 [ 0.214 -0.743  0.071 -0.196 -0.55 ]
 [-0.304 -0.433 -1.115  0.451  0.551]
 [ 0.589 -0.375 -0.645 -0.235  0.651]
 [ 0.354  0.426  0.292  0.1   -0.969]]