【机器学习】工业 4.0 下机器学习如何驱动智能制造升级

import random

# 定义产品种类数和设备数
n = 5  
m = 3  
# 随机初始化种群，每个个体是一个二维列表，代表一种调度方案
population = [[random.sample(range(n), n) for _ in range(m)] for _ in range(10)]  

def fitness(schedule):
    makespan = 0
    for machine in schedule:
        completion_times = [0] * n
        for i, product in enumerate(machine):
            start_time = max([completion_times[j] for j in range(i) if get_precedence(product, machine[j])])
            completion_times[i] = start_time + processing_time(product)
        makespan = max(completion_times)
    return makespan

# 计算种群中每个个体的适应度
fitness_values = [fitness(schedule) for schedule in population]  

# 选择操作，采用轮盘赌选择
def roulette_wheel_selection(population, fitness_values):
    total_fitness = sum(fitness_values)
    selection_probs = [fit / total_fitness for fit in fitness_values]
    selected_indices = random.choices(range(len(population)), weights=selection_probs, k=len(population))
    return [population[i] for i in selected_indices]

# 交叉操作
def crossover(parent1, parent2):
    crossover_point = random.randint(1, n - 1)
    child1 = parent1[:crossover_point] + parent2[crossover_point:]
    child2 = parent2[:crossover_point] + parent1[crossover_point:]
    return child1, child2

# 变异操作
def mutation(schedule):
    mutate_index = random.randint(0, len(schedule) - 1)
    mutate_position = random.randint(0, n - 1)
    schedule[mutate_index][mutate_position] = random.randint(0, n - 1)
    return schedule

new_population = []
for _ in range(len(population)):
    parent1, parent2 = random.sample(population, 2)
    child1, child2 = crossover(parent1, parent2)
    child1 = mutation(child1)
    child2 = mutation(child2)
    new_population.extend([child1, child2])

population = new_population

import tensorflow as tf
from tensorflow.keras import layers

model = tf.keras.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(image_height, image_width, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(128, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

model.compile(optimizer='adam',
              loss='binary_crossentropy',
              metrics=['accuracy'])

# 假设已有训练图像数据 train_images、训练标签 train_labels 和测试图像数据 test_images、测试标签 test_labels
model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels))

import statsmodels.api as sm

# 模拟的设备运行数据，每行代表一个时刻的数据，每列代表一个参数
X = np.array([[100, 200, 30], [120, 220, 35],...])  
# 对应的能源消耗数据
y = np.array([500, 550,...])  

X = sm.add_constant(X)
model = sm.OLS(y, X).fit()

# 预测新时刻的能源消耗
new_X = np.array([[130, 230, 40]])  
new_X = sm.add_constant(new_X)
predicted_y = model.predict(new_X)
print(f"预测能源消耗: {predicted_y[0]}")

import numpy as np
from sklearn.linear_model import LinearRegression

# 模拟的设备运行时间数据
x = np.array([100, 200, 300, 400, 500]).reshape(-1, 1)  
# 模拟的对应剩余使用寿命数据
y = np.array([500, 400, 300, 200, 100])  

model = LinearRegression()
model.fit(x, y)

# 预测新的设备剩余使用寿命
new_x = np.array([600]).reshape(-1, 1)  
predicted_y = model.predict(new_x)
print(f"预测的剩余使用寿命: {predicted_y[0]}")

import tensorflow as tf
from tensorflow.keras import layers

# 构建模型
model = tf.keras.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(image_height, image_width, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(num_classes, activation='softmax'))

# 编译模型
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# 训练模型（假设已有训练数据 train_images, train_labels 和测试数据 test_images, test_labels）
model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels))

import statsmodels.api as sm

# 模拟的历史销售数据
sales_data = [100, 120, 130, 150, 140, 160, 180, 200]  
# 将数据转换为时间序列
ts = sm.tsa.Series(sales_data)  
model = sm.tsa.ARIMA(ts, order=(1, 1, 1))
results = model.fit()

# 预测未来步骤
forecast = results.forecast(steps=3)  
print(f"未来需求预测: {forecast}")