使用Python实现深度学习模型：智能水质监测与管理

pip install tensorflow pandas numpy matplotlib scikit-learn

import pandas as pd

# 读取数据
data = pd.read_csv('water_quality_data.csv')

# 查看数据结构
print(data.head())

import numpy as np
from sklearn.preprocessing import MinMaxScaler

# 数据清洗
data = data.dropna()

# 特征提取
features = data[['temperature', 'pH', 'dissolved_oxygen', 'conductivity', 'turbidity']]
labels = data['quality_label']

# 数据归一化
scaler = MinMaxScaler()
features_scaled = scaler.fit_transform(features)

# 将数据分为训练集和测试集
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(features_scaled, labels, test_size=0.2, random_state=42)

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv1D, MaxPooling1D, Flatten

# 构建模型
def build_model(input_shape):
    model = Sequential([
        Conv1D(32, 3, activation='relu', input_shape=input_shape),
        MaxPooling1D(2),
        Conv1D(64, 3, activation='relu'),
        MaxPooling1D(2),
        Flatten(),
        Dense(128, activation='relu'),
        Dense(1, activation='sigmoid')
    ])
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    return model

# 示例
input_shape = (X_train.shape[1], 1)
X_train_reshaped = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))
X_test_reshaped = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))

model = build_model(input_shape)

# 训练模型
model.fit(X_train_reshaped, y_train, epochs=10, validation_data=(X_test_reshaped, y_test))

# 评估模型
loss, accuracy = model.evaluate(X_test_reshaped, y_test)
print(f'Test Loss: {loss}')
print(f'Test Accuracy: {accuracy}')

import time

def collect_real_time_data():
    # 模拟实时数据采集
    while True:
        temperature = np.random.uniform(15, 30)
        pH = np.random.uniform(6, 9)
        dissolved_oxygen = np.random.uniform(5, 10)
        conductivity = np.random.uniform(100, 500)
        turbidity = np.random.uniform(0, 5)
        data = np.array([[temperature, pH, dissolved_oxygen, conductivity, turbidity]])
        data_scaled = scaler.transform(data)
        data_reshaped = data_scaled.reshape((data_scaled.shape[0], data_scaled.shape[1], 1))
        yield data_reshaped
        time.sleep(1)

# 示例
data_generator = collect_real_time_data()

def real_time_monitoring(model, data_generator):
    for data in data_generator:
        prediction = model.predict(data)
        if prediction > 0.5:
            print("Warning: Poor water quality detected!")
        else:
            print("Water quality is good.")

# 示例
real_time_monitoring(model, data_generator)

Smart_Water_Quality_Monitoring/
│
├── main.py          # 主程序文件
├── water_quality_data.csv # 数据文件
├── model/           # 模型文件夹
│   └── water_quality_model.h5
└── utils/           # 工具文件夹（如数据预处理脚本等）