import cvxpy as cpimport numpy as npimport math# 定义变量x = cpVariableb = cpVariablen = cpVariable# 定义常数_a = mathradians15_r = mathradians120 2_D = 110 # 定义约束条件和目标函数constraints = x = -09 x = -08

这个错误是由于numpy模块中没有名为atan的属性导致的。正确的函数名应该是arctan。你可以将代码中的np.atan修改为np.arctan来修复这个错误。修改后的代码如下：

import cvxpy as cp
import numpy as np
import math

# 定义变量
x = cp.Variable()
b = cp.Variable()
n = cp.Variable()

# 定义常数
_a = math.radians(1.5)
_r = math.radians(120 / 2)
_D = 110  

# 定义约束条件和目标函数
constraints = [
    x >= -0.9, x <= -0.8,
    b >= 0, b <= 2 * math.pi,
    n >= -18520, n <= 18520
]

objective = cp.Minimize(
    n * x * np.cos(np.arctan(np.cos(b) * np.tan(_a)) + _r) / np.cos(np.arctan(np.cos(b) * np.tan(_a))) * np.sin(_r) * x - 2*_D
)

# 定义优化问题
problem = cp.Problem(objective, constraints)

# 求解优化问题
problem.solve()

# 输出结果
print("最优解: x =", x.value, ", b =", b.value, ", n =", n.value)
print("最优目标函数值:", problem.value)

请注意，你可能还需要确保你的环境中已经安装了cvxpy和numpy模块。可以使用pip install cvxpy numpy命令进行安装。