import tkinter as tk
from tkinter import ttk, filedialog, messagebox
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense, Lambda
from tensorflow.keras.optimizers import Adam
from sklearn.preprocessing import MinMaxScaler
import os
import time
import warnings
import matplotlib.dates as mdates
warnings.filterwarnings('ignore', category=UserWarning, module='tensorflow')
mpl.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'Arial Unicode MS']
mpl.rcParams['axes.unicode_minus'] = False # 关键修复：使用 ASCII 减号
# 设置中文字体支持
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
class PINNModel(tf.keras.Model):
def __init__(self, num_layers=4, hidden_units=32, dropout_rate=0.1, l2_reg=0.001, **kwargs):
super(PINNModel, self).__init__(**kwargs)
# 增加输入维度处理能力
self.dense_layers = []
self.bn_layers = [] # 批量归一化层
self.dropout_layers = []
# 添加L2正则化
self.l2_reg = l2_reg
# 创建更深的网络结构
for i in range(num_layers):
# 第一层使用更大的维度
units = hidden_units * 2 if i == 0 else hidden_units
self.dense_layers.append(
Dense(units, activation='swish', # 使用Swish激活函数
kernel_regularizer=tf.keras.regularizers.l2(l2_reg))
)
self.bn_layers.append(tf.keras.layers.BatchNormalization())
self.dropout_layers.append(tf.keras.layers.Dropout(dropout_rate))
# 添加跳跃连接层
self.residual_layer = Dense(hidden_units, activation='linear')
# 最终输出层
self.final_layer = Dense(1, activation='linear',
kernel_regularizer=tf.keras.regularizers.l2(l2_reg))
# 物理参数优化 - 使用更灵活的参数化方法
# 基本衰减系数 (使用指数确保正值)
self.k1_log = tf.Variable(tf.math.log(0.1), trainable=True, dtype=tf.float32, name='k1_log')
# 水位依赖的衰减系数 (使用softplus确保正值)
self.k2_raw = tf.Variable(0.01, trainable=True, dtype=tf.float32, name='k2_raw')
# 非线性项系数 (使用sigmoid约束在[0,1])
self.alpha_raw = tf.Variable(0.1, trainable=True, dtype=tf.float32, name='alpha_raw')
# 外部影响系数 (使用tanh约束在[-0.2,0.2])
self.beta_raw = tf.Variable(0.05, trainable=True, dtype=tf.float32, name='beta_raw')
@property
def k1(self):
"""指数变换确保正值"""
return tf.exp(self.k1_log)
@property
def k2(self):
"""Softplus变换确保正值"""
return tf.math.softplus(self.k2_raw) * 0.1 # 约束在0-0.1之间
@property
def alpha(self):
"""Sigmoid约束在[0,1]"""
return tf.math.sigmoid(self.alpha_raw)
@property
def beta(self):
"""Tanh约束在[-0.2,0.2]"""
return tf.math.tanh(self.beta_raw) * 0.2
def call(self, inputs, training=False):
# 解包输入
t, h, dt, lag1, lag3, lag7, month_feats, season_feats = inputs
# 组合所有特征
x = tf.concat([
t, h, dt,
lag1, lag3, lag7,
month_feats, season_feats,
t * h, h * dt, t * dt, (t * h * dt),
h * lag1, h * lag3, h * lag7,
dt * lag1, dt * lag3, dt * lag7
], axis=1)
# 初始投影层
x_proj = Dense(64, activation='swish')(x)
# 残差块
residual = self.residual_layer(x_proj)
# 通过所有隐藏层（带批量归一化和dropout）
for i, (dense_layer, bn_layer, dropout_layer) in enumerate(zip(
self.dense_layers, self.bn_layers, self.dropout_layers)):
# 第一层使用残差连接
if i == 0:
x = dense_layer(x_proj)
x = bn_layer(x, training=training)
x = dropout_layer(x, training=training)
x = x + residual # 残差连接
else:
x = dense_layer(x)
x = bn_layer(x, training=training)
x = dropout_layer(x, training=training)
# 注意力机制 - 增强重要特征
attention = Dense(x.shape[-1], activation='sigmoid')(x)
x = x * attention
return self.final_layer(x)
def physics_loss(self, t, h_current, dt, training=False):
"""改进的物理损失计算"""
# 创建零值占位符
batch_size = tf.shape(t)[0]
zeros = tf.zeros((batch_size, 1), dtype=tf.float32)
zeros2 = tf.zeros((batch_size, 2), dtype=tf.float32)
# 预测下一时刻水位
h_next_pred = self([t, h_current, dt, zeros, zeros, zeros, zeros2, zeros2],
training=training)
# 物理方程计算 - 使用改进的公式
# 非线性衰减项
decay_factor = self.k1 + self.k2 * h_current
exponent = -decay_factor * dt
exponent = tf.clip_by_value(exponent, -50.0, 50.0)
decay_term = h_current * tf.exp(exponent)
# 外部影响项（考虑时间依赖性）
external_factor = self.alpha * self.beta * dt
external_factor = tf.clip_by_value(external_factor, -10.0, 10.0)
external_term = self.alpha * (1 - tf.exp(-external_factor))
# 残差计算
residual = h_next_pred - (decay_term + external_term)
# 添加物理参数正则化（鼓励简单物理模型）
param_reg = 0.01 * (tf.abs(self.k1) + tf.abs(self.k2) + tf.abs(self.alpha) + tf.abs(self.beta))
return tf.reduce_mean(tf.square(residual)) + param_reg
class DamSeepageModel:
def __init__(self, root):
self.root = root
self.root.title("大坝渗流预测模型(PINNs)")
self.root.geometry("1200x800")
# 初始化数据
self.train_df = None # 训练集
self.test_df = None # 测试集
self.model = None
self.scaler_t = MinMaxScaler(feature_range=(0, 1))
self.scaler_h = MinMaxScaler(feature_range=(0, 1))
self.scaler_dt = MinMaxScaler(feature_range=(0, 1))
self.evaluation_metrics = {}
# 创建主界面
self.create_widgets()
def create_widgets(self):
# 创建主框架
main_frame = ttk.Frame(self.root, padding=10)
main_frame.pack(fill=tk.BOTH, expand=True)
# 左侧控制面板
control_frame = ttk.LabelFrame(main_frame, text="模型控制", padding=10)
control_frame.pack(side=tk.LEFT, fill=tk.Y, padx=5, pady=5)
# 文件选择部分
file_frame = ttk.LabelFrame(control_frame, text="数据文件", padding=10)
file_frame.pack(fill=tk.X, pady=5)
# 训练集选择
ttk.Label(file_frame, text="训练集:").grid(row=0, column=0, sticky=tk.W, pady=5)
self.train_file_var = tk.StringVar()
ttk.Entry(file_frame, textvariable=self.train_file_var, width=30, state='readonly').grid(
row=0, column=1,
padx=5)
ttk.Button(file_frame, text="选择文件", command=lambda: self.select_file("train")).grid(row=0, column=2)
# 测试集选择
ttk.Label(file_frame, text="测试集:").grid(row=1, column=0, sticky=tk.W, pady=5)
self.test_file_var = tk.StringVar()
ttk.Entry(file_frame, textvariable=self.test_file_var, width=30, state='readonly').grid(row=1, column=1, padx=5)
ttk.Button(file_frame, text="选择文件", command=lambda: self.select_file("test")).grid(row=1, column=2)
# PINNs参数设置
param_frame = ttk.LabelFrame(control_frame, text="PINNs参数", padding=10)
param_frame.pack(fill=tk.X, pady=10)
# 验证集切分比例
ttk.Label(param_frame, text="验证集比例:").grid(row=0, column=0, sticky=tk.W, pady=5)
self.split_ratio_var = tk.DoubleVar(value=0.2)
ttk.Spinbox(param_frame, from_=0, to=1, increment=0.05,
textvariable=self.split_ratio_var, width=10).grid(row=0, column=1, padx=5)
# 隐藏层数量
ttk.Label(param_frame, text="网络层数:").grid(row=1, column=0, sticky=tk.W, pady=5)
self.num_layers_var = tk.IntVar(value=4)
ttk.Spinbox(param_frame, from_=2, to=8, increment=1,
textvariable=self.num_layers_var, width=10).grid(row=1, column=1, padx=5)
# 每层神经元数量
ttk.Label(param_frame, text="神经元数/层:").grid(row=2, column=0, sticky=tk.W, pady=5)
self.hidden_units_var = tk.IntVar(value=32)
ttk.Spinbox(param_frame, from_=16, to=128, increment=4,
textvariable=self.hidden_units_var, width=10).grid(row=2, column=1, padx=5)
# 训练轮次
ttk.Label(param_frame, text="训练轮次:").grid(row=3, column=0, sticky=tk.W, pady=5)
self.epochs_var = tk.IntVar(value=500)
ttk.Spinbox(param_frame, from_=100, to=2000, increment=100,
textvariable=self.epochs_var, width=10).grid(row=3, column=1, padx=5)
# 物理损失权重
ttk.Label(param_frame, text="物理损失权重:").grid(row=4, column=0, sticky=tk.W, pady=5)
self.physics_weight_var = tk.DoubleVar(value=0.5)
ttk.Spinbox(param_frame, from_=0.1, to=1.0, increment=0.1,
textvariable=self.physics_weight_var, width=10).grid(row=4, column=1, padx=5)
# 控制按钮
btn_frame = ttk.Frame(control_frame)
btn_frame.pack(fill=tk.X, pady=10)
ttk.Button(btn_frame, text="训练模型", command=self.train_model).pack(side=tk.LEFT, padx=5)
ttk.Button(btn_frame, text="预测结果", command=self.predict).pack(side=tk.LEFT, padx=5)
ttk.Button(btn_frame, text="保存结果", command=self.save_results).pack(side=tk.LEFT, padx=5)
ttk.Button(btn_frame, text="重置", command=self.reset).pack(side=tk.RIGHT, padx=5)
# 状态栏
self.status_var = tk.StringVar(value="就绪")
status_bar = ttk.Label(control_frame, textvariable=self.status_var, relief=tk.SUNKEN, anchor=tk.W)
status_bar.pack(fill=tk.X, side=tk.BOTTOM)
# 右侧结果显示区域
result_frame = ttk.Frame(main_frame)
result_frame.pack(side=tk.RIGHT, fill=tk.BOTH, expand=True, padx=5, pady=5)
# 创建标签页
self.notebook = ttk.Notebook(result_frame)
self.notebook.pack(fill=tk.BOTH, expand=True)
# 损失曲线标签页
self.loss_frame = ttk.Frame(self.notebook)
self.notebook.add(self.loss_frame, text="训练损失")
# 预测结果标签页
self.prediction_frame = ttk.Frame(self.notebook)
self.notebook.add(self.prediction_frame, text="预测结果")
# 指标显示
self.metrics_var = tk.StringVar()
metrics_label = ttk.Label(
self.prediction_frame,
textvariable=self.metrics_var,
font=('TkDefaultFont', 10, 'bold'),
relief='ridge',
padding=5
)
metrics_label.pack(fill=tk.X, padx=5, pady=5)
# 初始化绘图区域
self.fig, self.ax = plt.subplots(figsize=(10, 6))
self.canvas = FigureCanvasTkAgg(self.fig, master=self.prediction_frame)
self.canvas.get_tk_widget().pack(fill=tk.BOTH, expand=True)
# 损失曲线画布
self.loss_fig, self.loss_ax = plt.subplots(figsize=(10, 4))
self.loss_canvas = FigureCanvasTkAgg(self.loss_fig, master=self.loss_frame)
self.loss_canvas.get_tk_widget().pack(fill=tk.BOTH, expand=True)
def select_file(self, file_type):
"""选择Excel文件并计算时间步长"""
try:
file_path = filedialog.askopenfilename(
title=f"选择{file_type}集Excel文件",
filetypes=[("Excel文件", "*.xlsx *.xls"), ("所有文件", "*.*")]
)
if not file_path:
return
df = pd.read_excel(file_path)
# 验证必需列是否存在
required_cols = ['year', 'month', 'day', '水位']
missing_cols = [col for col in required_cols if col not in df.columns]
if missing_cols:
messagebox.showerror("列名错误", f"缺少必需列: {', '.join(missing_cols)}")
return
# 时间特征处理
time_features = ['year', 'month', 'day']
missing_time_features = [feat for feat in time_features if feat not in df.columns]
if missing_time_features:
messagebox.showerror("列名错误",
f"Excel文件缺少预处理后的时间特征列: {', '.join(missing_time_features)}")
return
# 创建时间戳列 (增强兼容性)
time_cols = ['year', 'month', 'day']
if 'hour' in df.columns: time_cols.append('hour')
if 'minute' in df.columns: time_cols.append('minute')
if 'second' in df.columns: time_cols.append('second')
# 填充缺失的时间单位
for col in ['hour', 'minute', 'second']:
if col not in df.columns:
df[col] = 0
df['datetime'] = pd.to_datetime(df[time_cols])
# 设置时间索引
df = df.set_index('datetime')
# 计算相对时间（天）
df['days'] = (df.index - df.index[0]).days
# 新增：计算时间步长dt（单位：天）
df['dt'] = df.index.to_series().diff().dt.total_seconds() / 86400 # 精确到秒级
# 处理时间步长异常值
if len(df) > 1:
# 计算有效时间步长（排除<=0的值）
valid_dt = df['dt'][df['dt'] > 0]
if len(valid_dt) > 0:
avg_dt = valid_dt.mean()
else:
avg_dt = 1.0
else:
avg_dt = 1.0
# 替换非正值
df.loc[df['dt'] <= 0, 'dt'] = avg_dt
# 填充缺失值
df['dt'] = df['dt'].fillna(avg_dt)
# 添加滞后特征 (1天、3天、7天的水位)
df['水位_lag1'] = df['水位'].shift(1)
df['水位_lag3'] = df['水位'].shift(3)
df['水位_lag7'] = df['水位'].shift(7)
# 填充缺失的滞后值（用第一个有效值向后填充）
lag_cols = ['水位_lag1', '水位_lag3', '水位_lag7']
df[lag_cols] = df[lag_cols].fillna(method='bfill')
# 添加周期性特征
df['month'] = df.index.month
df['day_of_year'] = df.index.dayofyear
# 月份的正弦/余弦变换
df['month_sin'] = np.sin(2 * np.pi * df['month'] / 12)
df['month_cos'] = np.cos(2 * np.pi * df['month'] / 12)
# 季节特征（每3个月一个季节）
seasons = [(12, 1, 2), (3, 4, 5), (6, 7, 8), (9, 10, 11)]
season_map = {}
for i, months in enumerate(seasons):
for month in months:
season_map[month] = i
df['season'] = df['month'].map(season_map)
# 季节的正弦/余弦变换
df['season_sin'] = np.sin(2 * np.pi * df['season'] / 4)
df['season_cos'] = np.cos(2 * np.pi * df['season'] / 4)
# 保存数据
if file_type == "train":
self.train_df = df
self.train_file_var.set(os.path.basename(file_path))
self.status_var.set(f"已加载训练集: {len(self.train_df)}条数据")
else:
self.test_df = df
self.test_file_var.set(os.path.basename(file_path))
self.status_var.set(f"已加载测试集: {len(self.test_df)}条数据")
except Exception as e:
error_msg = f"文件读取失败: {str(e)}\n\n请确保：\n1. 文件不是打开状态\n2. 文件格式正确\n3. 包含必需的时间和水位列"
messagebox.showerror("文件错误", error_msg)
def calculate_metrics(self, y_true, y_pred):
"""计算评估指标"""
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
mse = mean_squared_error(y_true, y_pred)
rmse = np.sqrt(mse)
mae = mean_absolute_error(y_true, y_pred)
non_zero_idx = np.where(y_true != 0)[0]
if len(non_zero_idx) > 0:
mape = np.mean(np.abs((y_true[non_zero_idx] - y_pred[non_zero_idx]) / y_true[non_zero_idx])) * 100
else:
mape = float('nan')
r2 = r2_score(y_true, y_pred)
return {
'MSE': mse,
'RMSE': rmse,
'MAE': mae,
'MAPE': mape, # 修正键名
'R2': r2
}
def train_model(self):
"""训练PINNs模型（带早停机制+训练指标监控）"""
if self.train_df is None:
messagebox.showwarning("警告", "请先选择训练集文件")
return
try:
self.status_var.set("正在预处理数据...")
self.root.update()
# 从训练集中切分训练子集和验证子集（时间顺序切分）
split_ratio = 1 - self.split_ratio_var.get()
split_idx = int(len(self.train_df) * split_ratio)
train_subset = self.train_df.iloc[:split_idx]
valid_subset = self.train_df.iloc[split_idx:]
# 检查数据量是否足够
if len(train_subset) < 2 or len(valid_subset) < 2:
messagebox.showerror("数据错误", "训练集数据量不足（至少需要2个时间步）")
return
# ===== 新增：创建特征归一化器 =====
self.scaler_lag1 = MinMaxScaler(feature_range=(0, 1))
self.scaler_lag3 = MinMaxScaler(feature_range=(0, 1))
self.scaler_lag7 = MinMaxScaler(feature_range=(0, 1))
self.scaler_month = MinMaxScaler(feature_range=(0, 1))
self.scaler_season = MinMaxScaler(feature_range=(0, 1))
# 数据预处理 - 分别归一化不同特征
# 时间特征
t_train = train_subset['days'].values[1:].reshape(-1, 1)
self.scaler_t.fit(t_train)
t_train_scaled = self.scaler_t.transform(t_train).astype(np.float32)
# 水位特征
h_train = train_subset['水位'].values[:-1].reshape(-1, 1)
self.scaler_h.fit(h_train)
h_train_scaled = self.scaler_h.transform(h_train).astype(np.float32)
# 时间步长特征
dt_train = train_subset['dt'].values[1:].reshape(-1, 1)
self.scaler_dt.fit(dt_train)
dt_train_scaled = self.scaler_dt.transform(dt_train).astype(np.float32)
# ===== 新增：归一化滞后特征 =====
lag1_train = train_subset['水位_lag1'].values[:-1].reshape(-1, 1)
self.scaler_lag1.fit(lag1_train)
lag1_train_scaled = self.scaler_lag1.transform(lag1_train).astype(np.float32)
lag3_train = train_subset['水位_lag3'].values[:-1].reshape(-1, 1)
self.scaler_lag3.fit(lag3_train)
lag3_train_scaled = self.scaler_lag3.transform(lag3_train).astype(np.float32)
lag7_train = train_subset['水位_lag7'].values[:-1].reshape(-1, 1)
self.scaler_lag7.fit(lag7_train)
lag7_train_scaled = self.scaler_lag7.transform(lag7_train).astype(np.float32)
# ===== 新增：归一化周期性特征 =====
month_sin_train = train_subset['month_sin'].values[:-1].reshape(-1, 1)
month_cos_train = train_subset['month_cos'].values[:-1].reshape(-1, 1)
month_features_train = np.hstack([month_sin_train, month_cos_train])
self.scaler_month.fit(month_features_train)
month_features_train_scaled = self.scaler_month.transform(month_features_train).astype(np.float32)
season_sin_train = train_subset['season_sin'].values[:-1].reshape(-1, 1)
season_cos_train = train_subset['season_cos'].values[:-1].reshape(-1, 1)
season_features_train = np.hstack([season_sin_train, season_cos_train])
self.scaler_season.fit(season_features_train)
season_features_train_scaled = self.scaler_season.transform(season_features_train).astype(np.float32)
# 归一化标签（下一时刻水位）
h_next_train = train_subset['水位'].values[1:].reshape(-1, 1)
h_next_train_scaled = self.scaler_h.transform(h_next_train).astype(np.float32)
# 准备验证数据（同样进行归一化）
t_valid = valid_subset['days'].values[1:].reshape(-1, 1)
t_valid_scaled = self.scaler_t.transform(t_valid).astype(np.float32)
h_valid = valid_subset['水位'].values[:-1].reshape(-1, 1)
h_valid_scaled = self.scaler_h.transform(h_valid).astype(np.float32)
dt_valid = valid_subset['dt'].values[1:].reshape(-1, 1)
dt_valid_scaled = self.scaler_dt.transform(dt_valid).astype(np.float32)
h_next_valid_scaled = self.scaler_h.transform(
valid_subset['水位'].values[1:].reshape(-1, 1)
).astype(np.float32)
# 原始值用于指标计算
h_next_train_true = h_next_train
h_next_valid_true = valid_subset['水位'].values[1:].reshape(-1, 1)
# 创建模型和优化器
self.model = PINNModel(
num_layers=self.num_layers_var.get(),
hidden_units=self.hidden_units_var.get()
)
# 创建动态学习率调度器
initial_lr = 0.001
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate=initial_lr,
decay_steps=100, # 每100步衰减一次
decay_rate=0.95, # 衰减率
staircase=True # 阶梯式衰减
)
optimizer = Adam(learning_rate=lr_schedule)
# ===== 新增：验证集的滞后特征归一化 =====
lag1_valid = valid_subset['水位_lag1'].values[:-1].reshape(-1, 1)
lag1_valid_scaled = self.scaler_lag1.transform(lag1_valid).astype(np.float32)
lag3_valid = valid_subset['水位_lag3'].values[:-1].reshape(-1, 1)
lag3_valid_scaled = self.scaler_lag3.transform(lag3_valid).astype(np.float32)
lag7_valid = valid_subset['水位_lag7'].values[:-1].reshape(-1, 1)
lag7_valid_scaled = self.scaler_lag7.transform(lag7_valid).astype(np.float32)
# ===== 新增：验证集的周期性特征归一化 =====
month_sin_valid = valid_subset['month_sin'].values[:-1].reshape(-1, 1)
month_cos_valid = valid_subset['month_cos'].values[:-1].reshape(-1, 1)
month_features_valid = np.hstack([month_sin_valid, month_cos_valid])
month_features_valid_scaled = self.scaler_month.transform(month_features_valid).astype(np.float32)
season_sin_valid = valid_subset['season_sin'].values[:-1].reshape(-1, 1)
season_cos_valid = valid_subset['season_cos'].values[:-1].reshape(-1, 1)
season_features_valid = np.hstack([season_sin_valid, season_cos_valid])
season_features_valid_scaled = self.scaler_season.transform(season_features_valid).astype(np.float32)
# 在训练循环中，使用归一化后的数据
train_dataset = tf.data.Dataset.from_tensor_slices(
((t_train_scaled, h_train_scaled, dt_train_scaled,
lag1_train_scaled, lag3_train_scaled, lag7_train_scaled,
month_features_train_scaled, season_features_train_scaled),
h_next_train_scaled)
)
train_dataset = train_dataset.shuffle(buffer_size=1024).batch(32)
valid_dataset = tf.data.Dataset.from_tensor_slices(
((t_valid_scaled, h_valid_scaled, dt_valid_scaled,
lag1_valid_scaled, lag3_valid_scaled, lag7_valid_scaled,
month_features_valid_scaled, season_features_valid_scaled),
h_next_valid_scaled)
)
valid_dataset = valid_dataset.batch(32)
# 初始化训练历史记录列表
train_data_loss_history = []
physics_loss_history = []
valid_data_loss_history = []
train_metrics_history = []
valid_metrics_history = []
# 早停机制参数
patience = int(self.epochs_var.get() / 3)
min_delta = 1e-4
best_valid_loss = float('inf')
wait = 0
best_epoch = 0
best_weights = None
start_time = time.time()
# 自定义训练循环
for epoch in range(self.epochs_var.get()):
# 获取当前学习率
current_lr = optimizer.learning_rate.numpy()
# 训练阶段
epoch_train_data_loss = []
epoch_physics_loss = []
train_pred_scaled = []
# 修改后的解包方式
# 在训练循环中：
for step, (inputs, h_next_batch) in enumerate(train_dataset):
t_batch, h_batch, dt_batch, lag1_batch, lag3_batch, lag7_batch, month_feats_batch, season_feats_batch = inputs
with tf.GradientTape() as tape:
# 预测下一时刻水位
h_pred = self.model([
t_batch, h_batch, dt_batch,
lag1_batch, lag3_batch, lag7_batch,
month_feats_batch, season_feats_batch
], training=True)
data_loss = tf.reduce_mean(tf.square(h_next_batch - h_pred))
# 计算物理损失
physics_loss = self.model.physics_loss(t_batch, h_batch, dt_batch, training=True)
# 使用设置的物理损失权重
current_physics_weight = self.physics_weight_var.get()
# 添加L2正则化损失
l2_loss = tf.reduce_sum(self.model.losses)
# 总损失
loss = data_loss + current_physics_weight * physics_loss + l2_loss
grads = tape.gradient(loss, self.model.trainable_variables)
optimizer.apply_gradients(zip(grads, self.model.trainable_variables))
epoch_train_data_loss.append(data_loss.numpy())
epoch_physics_loss.append(physics_loss.numpy())
train_pred_scaled.append(h_pred.numpy())
# 合并训练预测值
train_pred_scaled = np.concatenate(train_pred_scaled, axis=0)
train_pred_true = self.scaler_h.inverse_transform(train_pred_scaled)
train_metrics = self.calculate_metrics(
y_true=h_next_train_true.flatten(),
y_pred=train_pred_true.flatten()
)
train_metrics_history.append(train_metrics)
# 验证阶段
epoch_valid_data_loss = []
valid_pred_scaled = []
for (inputs, h_v_next_batch) in valid_dataset:
t_v_batch, h_v_batch, dt_v_batch, lag1_v_batch, lag3_v_batch, lag7_v_batch, month_feats_v_batch, season_feats_v_batch = inputs
h_v_pred = self.model([
t_v_batch, h_v_batch, dt_v_batch,
lag1_v_batch, lag3_v_batch, lag7_v_batch,
month_feats_v_batch, season_feats_v_batch
], training=False)
valid_data_loss = tf.reduce_mean(tf.square(h_v_next_batch - h_v_pred))
epoch_valid_data_loss.append(valid_data_loss.numpy())
valid_pred_scaled.append(h_v_pred.numpy())
# 合并验证预测值（归一化后）
valid_pred_scaled = np.concatenate(valid_pred_scaled, axis=0)
# 反归一化得到原始预测值
valid_pred_true = self.scaler_h.inverse_transform(valid_pred_scaled)
# 计算验证集指标（使用原始真实值和预测值）
valid_metrics = self.calculate_metrics(
y_true=h_next_valid_true.flatten(),
y_pred=valid_pred_true.flatten()
)
valid_metrics_history.append(valid_metrics)
# 计算平均损失
avg_train_data_loss = np.mean(epoch_train_data_loss)
avg_physics_loss = np.mean(epoch_physics_loss)
avg_valid_data_loss = np.mean(epoch_valid_data_loss)
# 记录损失
train_data_loss_history.append(avg_train_data_loss)
physics_loss_history.append(avg_physics_loss)
valid_data_loss_history.append(avg_valid_data_loss)
# 早停机制逻辑
current_valid_loss = avg_valid_data_loss
# 早停机制逻辑
current_valid_loss = avg_valid_data_loss
if current_valid_loss < best_valid_loss - min_delta:
best_valid_loss = current_valid_loss
best_epoch = epoch + 1
wait = 0
best_weights = self.model.get_weights()
else:
wait += 1
if wait >= patience:
self.status_var.set(f"触发早停！最佳轮次: {best_epoch}，最佳验证损失: {best_valid_loss:.4f}")
if best_weights is not None:
self.model.set_weights(best_weights)
break # 确保在此处退出循环
# 更新状态（添加当前学习率显示）
if epoch % 1 == 0:
# 提取当前训练/验证的关键指标
train_rmse = train_metrics['RMSE']
valid_rmse = valid_metrics['RMSE']
train_r2 = train_metrics['R2']
valid_r2 = valid_metrics['R2']
elapsed = time.time() - start_time
self.status_var.set(
f"训练中 | 轮次: {epoch + 1}/{self.epochs_var.get()} | "
f"学习率: {current_lr:.6f} | "
f"训练RMSE: {train_rmse:.4f} | 验证RMSE: {valid_rmse:.4f} | "
f"训练R²: {train_r2:.4f} | 验证R²: {valid_r2:.4f} | "
f"k1: {self.model.k1.numpy():.6f}, k2: {self.model.k2.numpy():.6f} | 时间: {elapsed:.1f}秒 | 早停等待: {wait}/{patience}"
)
self.root.update()
# 绘制损失曲线
self.loss_ax.clear()
epochs_range = range(1, len(train_data_loss_history) + 1)
self.loss_ax.plot(epochs_range, train_data_loss_history, 'b-', label='训练数据损失')
self.loss_ax.plot(epochs_range, physics_loss_history, 'r--', label='物理损失')
self.loss_ax.plot(epochs_range, valid_data_loss_history, 'g-.', label='验证数据损失')
self.loss_ax.set_title('PINNs训练与验证损失')
self.loss_ax.set_xlabel('轮次')
self.loss_ax.set_ylabel('损失', rotation=0)
self.loss_ax.legend()
self.loss_ax.grid(True, alpha=0.3)
self.loss_ax.set_yscale('log')
self.loss_canvas.draw()
# 训练完成提示
elapsed = time.time() - start_time
if wait >= patience:
completion_msg = (
f"早停触发 | 最佳轮次: {best_epoch} | 最佳验证损失: {best_valid_loss:.4f} | "
f"最佳验证RMSE: {valid_metrics_history[best_epoch - 1]['RMSE']:.4f} | "
f"总时间: {elapsed:.1f}秒"
)
else:
completion_msg = (
f"训练完成 | 总轮次: {self.epochs_var.get()} | "
f"最终训练RMSE: {train_metrics_history[-1]['RMSE']:.4f} | "
f"最终验证RMSE: {valid_metrics_history[-1]['RMSE']:.4f} | "
f"最终训练R²: {train_metrics_history[-1]['R2']:.4f} | "
f"最终验证R²: {valid_metrics_history[-1]['R2']:.4f} | "
f"总时间: {elapsed:.1f}秒"
)
# 保存训练历史
self.train_history = {
'train_data_loss': train_data_loss_history,
'physics_loss': physics_loss_history,
'valid_data_loss': valid_data_loss_history,
'train_metrics': train_metrics_history,
'valid_metrics': valid_metrics_history
}
# 保存学习到的物理参数
self.learned_params = {
"k1": self.model.k1.numpy(),
"k2": self.model.k2.numpy(),
"alpha": self.model.alpha.numpy(),
"beta": self.model.beta.numpy()
}
self.status_var.set(completion_msg)
messagebox.showinfo("训练完成", f"PINNs模型训练成功完成！\n{completion_msg}")
except Exception as e:
messagebox.showerror("训练错误", f"模型训练失败:\n{str(e)}")
self.status_var.set("训练失败")
def predict(self):
"""使用PINNs模型进行递归预测（带Teacher Forcing和蒙特卡洛Dropout）"""
if self.model is None:
messagebox.showwarning("警告", "请先训练模型")
return
if self.test_df is None:
messagebox.showwarning("警告", "请先选择测试集文件")
return
try:
self.status_var.set("正在生成预测(使用Teacher Forcing和MC Dropout)...")
self.root.update()
# 预处理测试数据 - 归一化
t_test = self.test_df['days'].values.reshape(-1, 1)
t_test_scaled = self.scaler_t.transform(t_test).astype(np.float32)
dt_test = self.test_df['dt'].values.reshape(-1, 1)
dt_test_scaled = self.scaler_dt.transform(dt_test).astype(np.float32)
h_test = self.test_df['水位'].values.reshape(-1, 1)
h_test_scaled = self.scaler_h.transform(h_test).astype(np.float32)
# ===== 新增：归一化测试集的滞后特征 =====
lag1_test = self.test_df['水位_lag1'].values.reshape(-1, 1)
lag1_test_scaled = self.scaler_lag1.transform(lag1_test).astype(np.float32)
lag3_test = self.test_df['水位_lag3'].values.reshape(-1, 1)
lag3_test_scaled = self.scaler_lag3.transform(lag3_test).astype(np.float32)
lag7_test = self.test_df['水位_lag7'].values.reshape(-1, 1)
lag7_test_scaled = self.scaler_lag7.transform(lag7_test).astype(np.float32)
# ===== 新增：归一化测试集的周期性特征 =====
month_sin_test = self.test_df['month_sin'].values.reshape(-1, 1)
month_cos_test = self.test_df['month_cos'].values.reshape(-1, 1)
month_features_test = np.hstack([month_sin_test, month_cos_test])
month_features_test_scaled = self.scaler_month.transform(month_features_test).astype(np.float32)
season_sin_test = self.test_df['season_sin'].values.reshape(-1, 1)
season_cos_test = self.test_df['season_cos'].values.reshape(-1, 1)
season_features_test = np.hstack([season_sin_test, season_cos_test])
season_features_test_scaled = self.scaler_season.transform(season_features_test).astype(np.float32)
# 改进的递归预测参数
n = len(t_test)
mc_iterations = 100
adaptive_forcing = True
# 存储蒙特卡洛采样结果
mc_predictions_scaled = np.zeros((mc_iterations, n, 1), dtype=np.float32)
# 进行多次蒙特卡洛采样
for mc_iter in range(mc_iterations):
predicted_scaled = np.zeros((n, 1), dtype=np.float32)
predicted_scaled[0] = h_test_scaled[0] # 第一个点使用真实值
# 递归预测（带自适应教师强制）
for i in range(1, n):
# 自适应教师强制：后期阶段增加真实值使用频率
if adaptive_forcing:
# 前期70%概率使用真实值，后期提高到90%
teacher_forcing_prob = 0.7 + 0.2 * min(1.0, i / (0.7 * n))
else:
teacher_forcing_prob = 0.7
# 决定使用真实值还是预测值
use_actual = np.random.rand() < teacher_forcing_prob
if use_actual and i < n - 1: # 不能使用未来值
h_prev = h_test_scaled[i - 1:i]
else:
h_prev = predicted_scaled[i - 1:i]
t_prev = t_test_scaled[i - 1:i]
dt_i = dt_test_scaled[i:i + 1]
# 准备输入特征
inputs = (
t_test_scaled[i - 1:i], # 时间
h_prev, # 当前水位
dt_test_scaled[i:i + 1], # 时间步长
lag1_test_scaled[i - 1:i],
lag3_test_scaled[i - 1:i],
lag7_test_scaled[i - 1:i],
month_features_test_scaled[i - 1:i],
season_features_test_scaled[i - 1:i]
)
# 直接传递整个输入元组
h_pred = self.model(inputs, training=True)
# 物理模型预测值（用于约束）
k1 = self.learned_params['k1']
k2 = self.learned_params['k2']
alpha = self.learned_params['alpha']
beta = self.learned_params['beta']
# 物理方程预测
exponent = - (k1 + k2 * h_prev) * dt_i
decay_term = h_prev * np.exp(exponent)
external_term = alpha * (1 - np.exp(-beta * dt_i))
physics_pred = decay_term + external_term
# 混合预测：神经网络预测与物理模型预测加权平均
physics_weight = 0.3 # 物理模型权重
final_pred = physics_weight * physics_pred + (1 - physics_weight) * h_pred.numpy()
predicted_scaled[i] = final_pred[0][0]
mc_predictions_scaled[mc_iter] = predicted_scaled
# 计算预测统计量
mean_pred_scaled = np.mean(mc_predictions_scaled, axis=0)
std_pred_scaled = np.std(mc_predictions_scaled, axis=0)
# 反归一化结果
predictions = self.scaler_h.inverse_transform(mean_pred_scaled)
uncertainty = self.scaler_h.inverse_transform(std_pred_scaled) * 1.96 # 95%置信区间
actual_values = h_test
test_time = self.test_df.index
# 清除现有图表
self.ax.clear()
# 计算合理的y轴范围 - 基于数据集中区域
# 获取实际值和预测值的中位数
median_val = np.median(actual_values)
# 计算数据的波动范围（标准差）
data_range = np.std(actual_values) * 4 # 4倍标准差覆盖大部分数据
# 设置y轴范围为中心值±数据波动范围
y_center = median_val
y_half_range = max(data_range, 10) # 确保最小范围为20个单位
y_min_adjusted = y_center - y_half_range
y_max_adjusted = y_center + y_half_range
# 确保范围不为零
if y_max_adjusted - y_min_adjusted < 1:
y_min_adjusted -= 5
y_max_adjusted += 5
# 绘制结果（带置信区间）
self.ax.plot(test_time, actual_values, 'b-', label='真实值', linewidth=2)
self.ax.plot(test_time, predictions, 'r--', label='预测均值', linewidth=2)
self.ax.fill_between(
test_time,
(predictions - uncertainty).flatten(),
(predictions + uncertainty).flatten(),
color='orange', alpha=0.3, label='95%置信区间'
)
# 设置自动调整的y轴范围
self.ax.set_ylim(y_min_adjusted, y_max_adjusted)
self.ax.set_title('大坝渗流水位预测(PINNs with MC Dropout)')
self.ax.set_xlabel('时间')
self.ax.set_ylabel('测压管水位', rotation=0)
self.ax.legend(loc='best') # 自动选择最佳位置
# 优化时间轴刻度
self.ax.xaxis.set_major_locator(mdates.YearLocator())
self.ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
self.ax.xaxis.set_minor_locator(mdates.MonthLocator(interval=2))
self.ax.grid(which='minor', axis='x', linestyle=':', color='gray', alpha=0.3)
self.ax.grid(which='major', axis='y', linestyle='-', color='lightgray', alpha=0.5)
self.ax.tick_params(axis='x', which='major', rotation=0, labelsize=9)
self.ax.tick_params(axis='x', which='minor', length=2)
# 计算评估指标（排除第一个点）
eval_actual = actual_values[1:].flatten()
eval_pred = predictions[1:].flatten()
self.evaluation_metrics = self.calculate_metrics(eval_actual, eval_pred)
# 添加不确定性指标
avg_uncertainty = np.mean(uncertainty)
max_uncertainty = np.max(uncertainty)
self.evaluation_metrics['Avg Uncertainty'] = avg_uncertainty
self.evaluation_metrics['Max Uncertainty'] = max_uncertainty
metrics_text = (
f"MSE: {self.evaluation_metrics['MSE']:.4f} | "
f"RMSE: {self.evaluation_metrics['RMSE']:.4f} | "
f"MAE: {self.evaluation_metrics['MAE']:.4f} | "
f"MAPE: {self.evaluation_metrics['MAPE']:.2f}% | "
f"R²: {self.evaluation_metrics['R2']:.4f}\n"
f"平均不确定性: {avg_uncertainty:.4f} | 最大不确定性: {max_uncertainty:.4f}"
)
self.metrics_var.set(metrics_text)
# 在图表上添加指标
self.ax.text(
0.5, 1.05, metrics_text,
transform=self.ax.transAxes,
ha='center', fontsize=8,
bbox=dict(facecolor='white', alpha=0.8)
)
params_text = (
f"物理参数: k1={self.learned_params['k1']:.4f}, "
f"k2={self.learned_params['k2']:.4f}, "
f"alpha={self.learned_params['alpha']:.4f}, "
f"beta={self.learned_params['beta']:.4f} | "
f"Teacher Forcing概率: {teacher_forcing_prob}"
)
self.ax.text(
0.5, 1.12, params_text,
transform=self.ax.transAxes,
ha='center', fontsize=8,
bbox=dict(facecolor='white', alpha=0.8)
)
# 调整布局
plt.tight_layout(pad=2.0)
self.canvas.draw()
# 保存预测结果
self.predictions = predictions
self.uncertainty = uncertainty
self.actual_values = actual_values
self.test_time = test_time
self.mc_predictions = mc_predictions_scaled
self.status_var.set(f"预测完成(MC Dropout采样{mc_iterations}次)")
except Exception as e:
messagebox.showerror("预测错误", f"预测失败:\n{str(e)}")
self.status_var.set("预测失败")
import traceback
traceback.print_exc()
def save_results(self):
"""保存预测结果和训练历史数据"""
if not hasattr(self, 'predictions') or not hasattr(self, 'train_history'):
messagebox.showwarning("警告", "请先生成预测结果并完成训练")
return
# 选择保存路径
save_path = filedialog.asksaveasfilename(
defaultextension=".xlsx",
filetypes=[("Excel文件", "*.xlsx"), ("所有文件", "*.*")],
title="保存结果"
)
if not save_path:
return
try:
# 1. 创建预测结果DataFrame
result_df = pd.DataFrame({
'时间': self.test_time,
'实际水位': self.actual_values.flatten(),
'预测水位': self.predictions.flatten()
})
# 2. 创建评估指标DataFrame
metrics_df = pd.DataFrame([self.evaluation_metrics])
# 3. 创建训练历史DataFrame
history_data = {
'轮次': list(range(1, len(self.train_history['train_data_loss']) + 1)),
'训练数据损失': self.train_history['train_data_loss'],
'物理损失': self.train_history['physics_loss'],
'验证数据损失': self.train_history['valid_data_loss']
}
# 添加训练集指标
for metric in ['MSE', 'RMSE', 'MAE', 'MAPE', 'R2']:
history_data[f'训练集_{metric}'] = [item[metric] for item in self.train_history['train_metrics']]
# 添加验证集指标
for metric in ['MSE', 'RMSE', 'MAE', 'MAPE', 'R2']:
history_data[f'验证集_{metric}'] = [item[metric] for item in self.train_history['valid_metrics']]
history_df = pd.DataFrame(history_data)
# 保存到Excel
with pd.ExcelWriter(save_path) as writer:
result_df.to_excel(writer, sheet_name='预测结果', index=False)
metrics_df.to_excel(writer, sheet_name='评估指标', index=False)
history_df.to_excel(writer, sheet_name='训练历史', index=False)
# 保存图表
chart_path = os.path.splitext(save_path)[0] + "_chart.png"
self.fig.savefig(chart_path, dpi=300)
# 保存损失曲线图
loss_path = os.path.splitext(save_path)[0] + "_loss.png"
self.loss_fig.savefig(loss_path, dpi=300)
self.status_var.set(f"结果已保存至: {os.path.basename(save_path)}")
messagebox.showinfo("保存成功",
f"预测结果和图表已保存至:\n"
f"主文件: {save_path}\n"
f"预测图表: {chart_path}\n"
f"损失曲线: {loss_path}")
except Exception as e:
messagebox.showerror("保存错误", f"保存结果失败:\n{str(e)}")
def reset(self):
# 重置归一化器
self.scaler_t = MinMaxScaler(feature_range=(0, 1))
self.scaler_h = MinMaxScaler(feature_range=(0, 1))
self.scaler_dt = MinMaxScaler(feature_range=(0, 1))
"""重置程序状态"""
self.train_df = None
self.test_df = None
self.model = None
self.train_file_var.set("")
self.test_file_var.set("")
# 清除训练历史
if hasattr(self, 'train_history'):
del self.train_history
# 清除图表
if hasattr(self, 'ax'):
self.ax.clear()
if hasattr(self, 'loss_ax'):
self.loss_ax.clear()
# 重绘画布
if hasattr(self, 'canvas'):
self.canvas.draw()
if hasattr(self, 'loss_canvas'):
self.loss_canvas.draw()
# 清除状态
self.status_var.set("已重置，请选择新数据")
# 清除预测结果
if hasattr(self, 'predictions'):
del self.predictions
# 清除指标文本
if hasattr(self, 'metrics_var'):
self.metrics_var.set("")
messagebox.showinfo("重置", "程序已重置，可以开始新的分析")
if __name__ == "__main__":
root = tk.Tk()
app = DamSeepageModel(root)
root.mainloop()
检查错误并改正
本文详细介绍了TensorFlow中的tf.reduce_sum函数，包括不同维度下的求和操作，以及tf.reduce_mean在降维和计算平均值中的应用。接着探讨了NumPy的argsorts函数，展示了如何按行或列对矩阵进行排序并获取排序后的索引。此外，还解释了np.concatenate在矩阵拼接中的用法，包括按行和按列拼接。最后，通过实例讲解了np.argmax函数如何找到数组中最大值的索引位置。
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