我认为问题是卷积计算不正确。

I think the problem is that the convolution calculation is incorrect.

import numpy as np
import scipy.optimize
import matplotlib.pyplot as plt

t = np.array([ -7200, -6300, -5400, -4500, -3600, -2700, -1800, -900, 0, 900, 1800, 2700, 3600, 4500, 5400, 6300, 7200, 8100, 9000, 9900, 10800])
g = np.array([ 4.7, 5.17, 4.93, 4.38, 4.47, 4.4, 3.36, 3.68, 4.58, 11.73, 18.23, 19.33, 19.04, 17.21, 12.98, 11.59, 9.26, 7.66, 6.59, 5.68, 5.1])
f = np.array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 8.25, 3, 0.5, 0, 0, 0, 0, 0, 0, 0, 0])

lambda_1 = 0.000431062
lambda_2 = 0.000580525

delta_t = 900

# Define the exponential parts of the integrals
x_1 = np.exp(-lambda_1 * t)
x_2 = np.exp(-lambda_2 * t)

# Define the convolution for a given 't' (in this case, using the index of 't')
def convolution(n, x):
    return np.dot(f[:n], x[:n][::-1])

# The integrals do not vary as part of the optimization, so calculate them now
integral_1 = delta_t * np.array([convolution(i, x_1) for i in range(len(t))])
integral_2 = delta_t * np.array([convolution(i, x_2) for i in range(len(t))])


#Definition of the function
def convol(n,A,B,C):
    return A * integral_1[n] + B * integral_2[n] + C

#Determination of fit parameters A,B,C
popt, pcov = scipy.optimize.curve_fit(convol, range(len(t)), g)
A,B,C= popt
perr = np.sqrt(np.diag(pcov))

# Print out the coefficients determined by the optimization
print(A, B, C)

#Plot fit
fit = convol(range(len(t)),A,B,C)
plt.plot(t, fit)
plt.scatter(t, g,s=50, color='black')
plt.show()

我得到的系数为：

A = 7.9742184468342304e-05
B = -1.0441976351760864e-05
C = 5.1089841502260178

我不知道B的负值是否合理，所以我将其保留照原样。如果您希望系数为正，则可以如James所示约束它们。

I don't know if the negative value for B is reasonable or not so I have left it as-is. If you want coefficients that are positive, you can constrain them as shown by James.