智慧矿山AI安全监管方案

# 导入所需的库
import cv2
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, LSTM

# 数据采集函数
def collect_data():
    # 通过摄像头获取顶板图像
    image = cv2.imread('顶板图像.jpg')
    # 从位移传感器获取边坡位移数据
    displacement = get_displacement_data()
    # 获取其他相关数据（地质数据、气象数据等）
    other_data = get_other_data()
    return image, displacement, other_data

# 数据预处理函数
def preprocess_data(image, displacement, other_data):
    # 图像灰度化
    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # 图像归一化
    normalized_image = gray_image / 255.0
    # 位移数据滤波
    filtered_displacement = filter_displacement(displacement)
    # 整合数据
    data = np.concatenate((normalized_image.flatten(), [filtered_displacement], other_data))
    return data

# 构建模型函数
def build_model():
    model = Sequential()
    # 添加卷积层
    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(image_height, image_width, 1)))
    model.add(MaxPooling2D((2, 2)))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2, 2)))
    model.add(Conv2D(128, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2, 2)))
    model.add(Flatten())
    # 添加全连接层
    model.add(Dense(128, activation='relu'))
    # 添加 LSTM 层处理时间序列数据
    model.add(LSTM(64))
    # 添加输出层
    model.add(Dense(1, activation='sigmoid'))
    return model

# 训练模型函数
def train_model(model, train_data, train_labels, epochs, batch_size):
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    model.fit(train_data, train_labels, epochs=epochs, batch_size=batch_size, validation_split=0.2)

# 风险评估与预警函数
def assess_risk(model, data):
    risk = model.predict(np.array([data]))[0][0]
    if risk > risk_threshold:
        send_warning()

# 主函数
if __name__ == '__main__':
    # 采集和预处理数据
    image, displacement, other_data = collect_data()
    data = preprocess_data(image, displacement, other_data)

    # 构建和训练模型
    model = build_model()
    train_data, train_labels = load_training_data()
    train_model(model, train_data, train_labels, epochs=100, batch_size=32)

    # 实时风险评估与预警
    while True:
        image, displacement, other_data = collect_data()
        data = preprocess_data(image, displacement, other_data)
        assess_risk(model, data)

# 导入所需的库
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neural_network import MLPClassifier

# 数据采集函数
def collect_data():
    # 从传感器获取设备运行数据
    vibration = get_vibration_data()
    temperature = get_temperature_data()
    pressure = get_pressure_data()
    # 获取设备运行状态标签
    label = get_device_status()
    return vibration, temperature, pressure, label

# 数据预处理函数
def preprocess_data(vibration, temperature, pressure):
    # 数据标准化
    scaler = StandardScaler()
    data = np.array([vibration, temperature, pressure]).T
    normalized_data = scaler.fit_transform(data)
    # 数据清洗（去除异常值和缺失值）
    cleaned_data = clean_data(normalized_data)
    return cleaned_data

# 特征提取函数
def extract_features(data):
    # 计算时域特征
    mean = np.mean(data, axis=0)
    variance = np.var(data, axis=0)
    peak = np.max(data, axis=0)
    # 计算频域特征（使用快速傅里叶变换 FFT）
    fft_data = np.fft.fft(data, axis=0)
    frequency = np.fft.fftfreq(data.shape[0])
    power_spectrum = np.abs(fft_data) ** 2
    # 合并特征
    features = np.concatenate((mean, variance, peak, power_spectrum))
    return features

# 构建模型函数
def build_model(model_type):
    if model_type =='svm':
        model = SVC(kernel='rbf', C=1.0, gamma='scale')
    elif model_type == 'random_forest':
        model = RandomForestClassifier(n_estimators=100, max_depth=5)
    elif model_type == 'neural_network':
        model = MLPClassifier(hidden_layer_sizes=(100, 50), activation='relu', solver='adam', max_iter=500)
    return model

# 训练模型函数
def train_model(model, train_data, train_labels):
    model.fit(train_data, train_labels)

# 故障诊断与预警函数
def diagnose_fault(model, data):
    prediction = model.predict(data)
    if prediction == 1:  # 假设 1 表示故障状态
        send_warning()

# 主函数
if __name__ == '__main__':
    # 采集和预处理数据
    vibration, temperature, pressure, label = collect_data()
    data = preprocess_data(vibration, temperature, pressure)

    # 特征提取
    features = extract_features(data)

    # 构建和训练模型
    model_type ='svm'  # 可选择不同的模型
    model = build_model(model_type)
    train_data, train_labels = load_training_data()
    train_model(model, train_data, train_labels)

    # 实时故障诊断与预警
    while True:
        vibration, temperature, pressure, _ = collect_data()
        data = preprocess_data(vibration, temperature, pressure)
        features = extract_features(data)
        diagnose_fault(model, [features])

# 导入所需的库
import cv2
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import load_model

# 数据采集函数
def collect_data():
    # 从摄像头获取视频帧
    frame = cv2.VideoCapture(0).read()[1]
    return frame

# 数据预处理函数
def preprocess_data(frame):
    # 图像裁剪和缩放
    resized_frame = cv2.resize(frame, (image_width, image_height))
    # 转换为灰度图像（可根据需要选择）
    gray_frame = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2GRAY)
    return gray_frame

# 构建模型函数（假设已经训练好模型并保存为 'person_behavior_model.h5'）
def build_model():
    model = load_model('person_behavior_model.h5')
    return model

# 行为识别与预警函数
def recognize_behavior(model, frame):
    # 对图像进行预处理
    processed_frame = preprocess_data(frame)
    # 扩展维度以匹配模型输入要求
    input_frame = np.expand_dims(processed_frame, axis=0)
    # 使用模型进行预测
    predictions = model.predict(input_frame)
    # 解析预测结果，获取人员位置和行为类别
    person_boxes, behavior_classes = parse_predictions(predictions)
    for box, behavior in zip(person_boxes, behavior_classes):
        if behavior == '未佩戴安全帽':  # 假设行为类别为 '未佩戴安全帽'
            send_warning(box)

# 主函数
if __name__ == '__main__':
    # 构建模型
    model = build_model()

    # 实时行为识别与预警
    while True:
        frame = collect_data()
        recognize_behavior(model, frame)