import numpy as np

# 定义模糊集合的隶属度参数
sludge = [0, 0.5, 1]    # 污泥浓度模糊集
grease = [0, 0.5, 1]    # 油脂浓度模糊集
time = [0, 0.25, 0.5, 0.75, 1]  # 洗涤时间模糊集

# 构建污泥和油脂的模糊关系矩阵（取最大值）
sludgeandgrease = np.zeros((len(sludge), len(grease)))
for i in range(len(sludge)):
    for j in range(len(grease)):
        sludgeandgrease[i, j] = max(sludge[i], grease[j])
sludgeandgrease = sludgeandgrease.reshape(9, 1)  # 展平为列向量

# 构建模糊关系矩阵 R（取最小值）
R = np.zeros((len(sludgeandgrease), len(time)))
for i in range(len(sludgeandgrease)):
    for j in range(len(time)):
        R[i, j] = min(sludgeandgrease[i], time[j])

# 输入模糊集的隶属度
x1 = [0, 0.83, 0.6]  # 污泥浓度实际测量隶属度
y1 = [0, 0.71, 0.7]  # 油脂浓度实际测量隶属度

# 计算输入条件的模糊关系（取最大值）
x1y1 = np.zeros((len(x1), len(y1)))
for i in range(len(x1)):
    for j in range(len(y1)):
        x1y1[i, j] = max(x1[i], y1[j])
x1y12 = x1y1.reshape(9)  # 展平为一维数组

# 合成模糊推理结果
result = np.zeros(5)
a = np.zeros(9)
for i in range(5):
    for j in range(9):
        a[j] = x1y12[j] * R[j, i]  # 模糊推理合成运算
    result[i] = max(a)  # 取最大值作为最终结果

print(result)