import streamlit as st import plotly.express as px import plotly.graph_objects as go import pandas as pd import numpy as np import joblib import os from datetime import datetime, timedelta from app.services.extruder_service import ExtruderService from app.services.main_process_service import MainProcessService from sklearn.preprocessing import StandardScaler, MinMaxScaler from sklearn.model_selection import train_test_split from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor from sklearn.svm import SVR from sklearn.neural_network import MLPRegressor # 导入稳态识别功能 class SteadyStateDetector: def __init__(self): pass def detect_steady_state(self, df, weight_col='米重', window_size=20, std_threshold=0.5, duration_threshold=60): """ 稳态识别逻辑：标记米重数据中的稳态段 :param df: 包含米重数据的数据框 :param weight_col: 米重列名 :param window_size: 滑动窗口大小（秒） :param std_threshold: 标准差阈值 :param duration_threshold: 稳态持续时间阈值（秒） :return: 包含稳态标记的数据框和稳态信息 """ if df is None or df.empty: return df, [] # 确保时间列是datetime类型 df['time'] = pd.to_datetime(df['time']) # 计算滚动统计量 df['rolling_std'] = df[weight_col].rolling(window=window_size, min_periods=5).std() df['rolling_mean'] = df[weight_col].rolling(window=window_size, min_periods=5).mean() # 计算波动范围 df['fluctuation_range'] = (df['rolling_std'] / df['rolling_mean']) * 100 df['fluctuation_range'] = df['fluctuation_range'].fillna(0) # 标记稳态点 df['is_steady'] = 0 steady_condition = ( (df['fluctuation_range'] < std_threshold) & (df[weight_col] >= 0.1) ) df.loc[steady_condition, 'is_steady'] = 1 # 识别连续稳态段 steady_segments = [] current_segment = {} for i, row in df.iterrows(): if row['is_steady'] == 1: if not current_segment: current_segment = { 'start_time': row['time'], 'start_idx': i, 'weights': [row[weight_col]] } else: current_segment['weights'].append(row[weight_col]) else: if current_segment: current_segment['end_time'] = df.loc[i-1, 'time'] if i > 0 else df.loc[i, 'time'] current_segment['end_idx'] = i-1 duration = (current_segment['end_time'] - current_segment['start_time']).total_seconds() if duration >= duration_threshold: weights_array = np.array(current_segment['weights']) current_segment['duration'] = duration current_segment['mean_weight'] = np.mean(weights_array) current_segment['std_weight'] = np.std(weights_array) current_segment['min_weight'] = np.min(weights_array) current_segment['max_weight'] = np.max(weights_array) current_segment['fluctuation_range'] = (current_segment['std_weight'] / current_segment['mean_weight']) * 100 # 计算置信度 confidence = 100 - (current_segment['fluctuation_range'] / std_threshold) * 50 confidence = max(50, min(100, confidence)) current_segment['confidence'] = confidence steady_segments.append(current_segment) current_segment = {} # 处理最后一个稳态段 if current_segment: current_segment['end_time'] = df['time'].iloc[-1] current_segment['end_idx'] = len(df) - 1 duration = (current_segment['end_time'] - current_segment['start_time']).total_seconds() if duration >= duration_threshold: weights_array = np.array(current_segment['weights']) current_segment['duration'] = duration current_segment['mean_weight'] = np.mean(weights_array) current_segment['std_weight'] = np.std(weights_array) current_segment['min_weight'] = np.min(weights_array) current_segment['max_weight'] = np.max(weights_array) current_segment['fluctuation_range'] = (current_segment['std_weight'] / current_segment['mean_weight']) * 100 confidence = 100 - (current_segment['fluctuation_range'] / std_threshold) * 50 confidence = max(50, min(100, confidence)) current_segment['confidence'] = confidence steady_segments.append(current_segment) # 在数据框中标记完整的稳态段 for segment in steady_segments: df.loc[segment['start_idx']:segment['end_idx'], 'is_steady'] = 1 return df, steady_segments def show_metered_weight_advanced(): # 初始化服务 extruder_service = ExtruderService() main_process_service = MainProcessService() # 页面标题 st.title("米重高级预测分析") # 初始化会话状态 if 'ma_start_date' not in st.session_state: st.session_state['ma_start_date'] = datetime.now().date() - timedelta(days=7) if 'ma_end_date' not in st.session_state: st.session_state['ma_end_date'] = datetime.now().date() if 'ma_quick_select' not in st.session_state: st.session_state['ma_quick_select'] = "最近7天" if 'ma_model_type' not in st.session_state: st.session_state['ma_model_type'] = 'RandomForest' if 'ma_sequence_length' not in st.session_state: st.session_state['ma_sequence_length'] = 10 if 'ma_use_steady_data' not in st.session_state: st.session_state['ma_use_steady_data'] = True if 'ma_steady_window' not in st.session_state: st.session_state['ma_steady_window'] = 20 if 'ma_steady_threshold' not in st.session_state: st.session_state['ma_steady_threshold'] = 0.5 # 默认特征列表（不再允许用户选择） default_features = ['螺杆转速', '机头压力', '流程主速', '螺杆温度', '后机筒温度', '前机筒温度', '机头温度'] # 定义回调函数 def update_dates(qs): st.session_state['ma_quick_select'] = qs today = datetime.now().date() if qs == "今天": st.session_state['ma_start_date'] = today st.session_state['ma_end_date'] = today elif qs == "最近3天": st.session_state['ma_start_date'] = today - timedelta(days=3) st.session_state['ma_end_date'] = today elif qs == "最近7天": st.session_state['ma_start_date'] = today - timedelta(days=7) st.session_state['ma_end_date'] = today elif qs == "最近30天": st.session_state['ma_start_date'] = today - timedelta(days=30) st.session_state['ma_end_date'] = today def on_date_change(): st.session_state['ma_quick_select'] = "自定义" # 查询条件区域 with st.expander("🔍 查询配置", expanded=True): # 添加自定义 CSS 实现响应式换行 st.markdown(""" """, unsafe_allow_html=True) # 创建布局 cols = st.columns([1, 1, 1, 1, 1, 1.5, 1.5, 1]) options = ["今天", "最近3天", "最近7天", "最近30天", "自定义"] for i, option in enumerate(options): with cols[i]: # 根据当前选择状态决定按钮类型 button_type = "primary" if st.session_state['ma_quick_select'] == option else "secondary" if st.button(option, key=f"btn_ma_{option}", width='stretch', type=button_type): update_dates(option) st.rerun() with cols[5]: start_date = st.date_input( "开始日期", label_visibility="collapsed", key="ma_start_date", on_change=on_date_change ) with cols[6]: end_date = st.date_input( "结束日期", label_visibility="collapsed", key="ma_end_date", on_change=on_date_change ) with cols[7]: query_button = st.button("🚀 开始分析", key="ma_query", width='stretch') # 模型配置 st.markdown("---") st.write("🤖 **模型配置**") model_cols = st.columns(2) with model_cols[0]: # 模型类型选择 model_options = ['RandomForest', 'GradientBoosting', 'SVR', 'MLP'] model_type = st.selectbox( "模型类型", options=model_options, key="ma_model_type", help="选择用于预测的模型类型" ) # 稳态识别配置 st.markdown("---") steady_cols = st.columns(3) with steady_cols[0]: st.write("⚖️ **稳态识别配置**") st.checkbox( "仅使用稳态数据进行训练", value=st.session_state['ma_use_steady_data'], key="ma_use_steady_data", help="启用后，只使用米重稳态时段的数据进行模型训练" ) with steady_cols[1]: st.write("📏 **稳态参数**") st.slider( "滑动窗口大小 (秒)", min_value=5, max_value=60, value=st.session_state['ma_steady_window'], step=5, key="ma_steady_window", help="用于稳态识别的滑动窗口大小" ) with steady_cols[2]: st.write("📊 **稳态阈值**") st.slider( "波动阈值 (%)", min_value=0.1, max_value=2.0, value=st.session_state['ma_steady_threshold'], step=0.1, key="ma_steady_threshold", help="稳态识别的波动范围阈值" ) # 转换为datetime对象 start_dt = datetime.combine(start_date, datetime.min.time()) end_dt = datetime.combine(end_date, datetime.max.time()) # 查询处理 if query_button: with st.spinner("正在获取数据..."): # 1. 获取完整的挤出机数据 df_extruder_full = extruder_service.get_extruder_data(start_dt, end_dt) # 2. 获取主流程控制数据 df_main_speed = main_process_service.get_cutting_setting_data(start_dt, end_dt) df_temp = main_process_service.get_temperature_control_data(start_dt, end_dt) # 检查是否有数据 has_data = any([ df_extruder_full is not None and not df_extruder_full.empty, df_main_speed is not None and not df_main_speed.empty, df_temp is not None and not df_temp.empty ]) if not has_data: st.warning("所选时间段内未找到任何数据，请尝试调整查询条件。") # 清除缓存数据 for key in ['cached_extruder_full', 'cached_main_speed', 'cached_temp', 'last_query_start', 'last_query_end']: if key in st.session_state: del st.session_state[key] return # 缓存数据到会话状态 st.session_state['cached_extruder_full'] = df_extruder_full st.session_state['cached_main_speed'] = df_main_speed st.session_state['cached_temp'] = df_temp st.session_state['last_query_start'] = start_dt st.session_state['last_query_end'] = end_dt # 数据处理和分析 if all(key in st.session_state for key in ['cached_extruder_full', 'cached_main_speed', 'cached_temp']): with st.spinner("正在分析数据..."): # 获取缓存数据 df_extruder_full = st.session_state['cached_extruder_full'] df_main_speed = st.session_state['cached_main_speed'] df_temp = st.session_state['cached_temp'] # 检查是否有数据 has_data = any([ df_extruder_full is not None and not df_extruder_full.empty, df_main_speed is not None and not df_main_speed.empty, df_temp is not None and not df_temp.empty ]) if not has_data: st.warning("所选时间段内未找到任何数据，请尝试调整查询条件。") return # 数据整合与预处理 def integrate_data(df_extruder_full, df_main_speed, df_temp): # 确保挤出机数据存在 if df_extruder_full is None or df_extruder_full.empty: return None # 创建只包含米重和时间的主数据集 df_merged = df_extruder_full[['time', 'metered_weight', 'screw_speed_actual', 'head_pressure']].copy() # 整合主流程数据 if df_main_speed is not None and not df_main_speed.empty: df_main_speed = df_main_speed[['time', 'process_main_speed']] df_merged = pd.merge_asof( df_merged.sort_values('time'), df_main_speed.sort_values('time'), on='time', direction='nearest', tolerance=pd.Timedelta('1min') ) # 整合温度数据 if df_temp is not None and not df_temp.empty: temp_cols = ['time', 'nakata_extruder_screw_display_temp', 'nakata_extruder_rear_barrel_display_temp', 'nakata_extruder_front_barrel_display_temp', 'nakata_extruder_head_display_temp'] df_temp_subset = df_temp[temp_cols].copy() df_merged = pd.merge_asof( df_merged.sort_values('time'), df_temp_subset.sort_values('time'), on='time', direction='nearest', tolerance=pd.Timedelta('1min') ) # 重命名列以提高可读性 df_merged.rename(columns={ 'screw_speed_actual': '螺杆转速', 'head_pressure': '机头压力', 'process_main_speed': '流程主速', 'nakata_extruder_screw_display_temp': '螺杆温度', 'nakata_extruder_rear_barrel_display_temp': '后机筒温度', 'nakata_extruder_front_barrel_display_temp': '前机筒温度', 'nakata_extruder_head_display_temp': '机头温度' }, inplace=True) # 清理数据 df_merged.dropna(subset=['metered_weight'], inplace=True) return df_merged # 执行数据整合 df_analysis = integrate_data(df_extruder_full, df_main_speed, df_temp) if df_analysis is None or df_analysis.empty: st.warning("数据整合失败，请检查数据质量或调整时间范围。") return # 重命名米重列 df_analysis.rename(columns={'metered_weight': '米重'}, inplace=True) # 稳态识别 steady_detector = SteadyStateDetector() # 获取稳态识别参数 use_steady_data = st.session_state.get('ma_use_steady_data', True) steady_window = st.session_state.get('ma_steady_window', 20) steady_threshold = st.session_state.get('ma_steady_threshold', 0.5) # 执行稳态识别 df_analysis_with_steady, steady_segments = steady_detector.detect_steady_state( df_analysis, weight_col='米重', window_size=steady_window, std_threshold=steady_threshold ) # 更新df_analysis为包含稳态标记的数据 df_analysis = df_analysis_with_steady # 稳态数据可视化 st.subheader("📈 稳态数据分布") # 创建稳态数据可视化图表 fig_steady = go.Figure() # 添加原始米重曲线 fig_steady.add_trace(go.Scatter( x=df_analysis['time'], y=df_analysis['米重'], name='原始米重', mode='lines', line=dict(color='lightgray', width=1) )) # 添加稳态数据点 steady_data_points = df_analysis[df_analysis['is_steady'] == 1] fig_steady.add_trace(go.Scatter( x=steady_data_points['time'], y=steady_data_points['米重'], name='稳态米重', mode='markers', marker=dict(color='green', size=3, opacity=0.6) )) # 添加非稳态数据点 non_steady_data_points = df_analysis[df_analysis['is_steady'] == 0] fig_steady.add_trace(go.Scatter( x=non_steady_data_points['time'], y=non_steady_data_points['米重'], name='非稳态米重', mode='markers', marker=dict(color='red', size=3, opacity=0.6) )) # 配置图表布局 fig_steady.update_layout( title="米重数据稳态分布", xaxis=dict(title="时间"), yaxis=dict(title="米重 (Kg/m)"), legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1), height=500 ) # 显示图表 st.plotly_chart(fig_steady, use_container_width=True) # 显示稳态统计 total_data = len(df_analysis) steady_data = len(df_analysis[df_analysis['is_steady'] == 1]) steady_ratio = (steady_data / total_data * 100) if total_data > 0 else 0 stats_cols = st.columns(3) stats_cols[0].metric("总数据量", total_data) stats_cols[1].metric("稳态数据量", steady_data) stats_cols[2].metric("稳态数据比例", f"{steady_ratio:.1f}%") # --- 原始数据趋势图 --- st.subheader("📈 原始数据趋势图") # 创建趋势图 fig_trend = go.Figure() # 添加米重数据 if df_extruder_full is not None and not df_extruder_full.empty: fig_trend.add_trace(go.Scatter( x=df_extruder_full['time'], y=df_extruder_full['metered_weight'], name='米重 (Kg/m)', mode='lines', line=dict(color='blue', width=2) )) # 添加螺杆转速 fig_trend.add_trace(go.Scatter( x=df_extruder_full['time'], y=df_extruder_full['screw_speed_actual'], name='螺杆转速 (RPM)', mode='lines', line=dict(color='green', width=1.5), yaxis='y2' )) # 添加机头压力 fig_trend.add_trace(go.Scatter( x=df_extruder_full['time'], y=df_extruder_full['head_pressure'], name='机头压力', mode='lines', line=dict(color='orange', width=1.5), yaxis='y3' )) # 添加流程主速 if df_main_speed is not None and not df_main_speed.empty: fig_trend.add_trace(go.Scatter( x=df_main_speed['time'], y=df_main_speed['process_main_speed'], name='流程主速 (M/Min)', mode='lines', line=dict(color='red', width=1.5), yaxis='y4' )) # 添加温度数据 if df_temp is not None and not df_temp.empty: # 螺杆温度 fig_trend.add_trace(go.Scatter( x=df_temp['time'], y=df_temp['nakata_extruder_screw_display_temp'], name='螺杆温度 (°C)', mode='lines', line=dict(color='purple', width=1), yaxis='y5' )) # 配置趋势图布局 fig_trend.update_layout( title='原始数据趋势', xaxis=dict( title='时间', rangeslider=dict(visible=True), type='date' ), yaxis=dict( title='米重 (Kg/m)', title_font=dict(color='blue'), tickfont=dict(color='blue') ), yaxis2=dict( title='螺杆转速 (RPM)', title_font=dict(color='green'), tickfont=dict(color='green'), overlaying='y', side='right' ), yaxis3=dict( title='机头压力', title_font=dict(color='orange'), tickfont=dict(color='orange'), overlaying='y', side='right', anchor='free', position=0.85 ), yaxis4=dict( title='流程主速 (M/Min)', title_font=dict(color='red'), tickfont=dict(color='red'), overlaying='y', side='right', anchor='free', position=0.75 ), yaxis5=dict( title='温度 (°C)', title_font=dict(color='purple'), tickfont=dict(color='purple'), overlaying='y', side='left', anchor='free', position=0.15 ), legend=dict( orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1 ), height=600, margin=dict(l=100, r=200, t=100, b=100), hovermode='x unified' ) # 显示趋势图 st.plotly_chart(fig_trend, width='stretch', config={'scrollZoom': True}) # --- 高级预测分析 --- st.subheader("📊 高级预测分析") # 检查所有默认特征是否在数据中 missing_features = [f for f in default_features if f not in df_analysis.columns] if missing_features: st.warning(f"数据中缺少以下特征: {', '.join(missing_features)}") else: try: # 准备数据 # 根据配置决定是否只使用稳态数据 use_steady_data = st.session_state.get('ma_use_steady_data', True) if use_steady_data: df_filtered = df_analysis[df_analysis['is_steady'] == 1] st.info(f"已过滤非稳态数据，使用 {len(df_filtered)} 条稳态数据进行训练") else: df_filtered = df_analysis.copy() # 首先确保df_analysis中没有NaN值 df_analysis_clean = df_filtered.dropna(subset=default_features + ['米重']) # 检查清理后的数据量 if len(df_analysis_clean) < 30: st.warning("数据量不足，无法进行有效的预测分析") else: # 创建一个新的DataFrame来存储所有特征和目标变量 all_features = df_analysis_clean[default_features + ['米重']].copy() # 清理所有NaN值 all_features_clean = all_features.dropna() # 检查清理后的数据量 if len(all_features_clean) < 20: st.warning("特征工程后数据量不足，无法进行有效的预测分析") else: # 分离特征和目标变量 feature_columns = [col for col in all_features_clean.columns if col != '米重'] X_final = all_features_clean[feature_columns] y_final = all_features_clean['米重'] # 检查最终数据量 if len(X_final) >= 20: # 分割训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X_final, y_final, test_size=0.2, random_state=42) # 数据标准化 scaler_X = StandardScaler() scaler_y = MinMaxScaler() X_train_scaled = scaler_X.fit_transform(X_train) X_test_scaled = scaler_X.transform(X_test) y_train_scaled = scaler_y.fit_transform(y_train.values.reshape(-1, 1)).ravel() y_test_scaled = scaler_y.transform(y_test.values.reshape(-1, 1)).ravel() # 模型训练 model = None y_pred = None if model_type == 'RandomForest': # 随机森林回归 model = RandomForestRegressor(n_estimators=100, random_state=42) model.fit(X_train, y_train) y_pred = model.predict(X_test) elif model_type == 'GradientBoosting': # 梯度提升回归 model = GradientBoostingRegressor(n_estimators=100, random_state=42) model.fit(X_train, y_train) y_pred = model.predict(X_test) elif model_type == 'SVR': # 支持向量回归 model = SVR(kernel='rbf', C=1.0, gamma='scale') model.fit(X_train_scaled, y_train_scaled) y_pred_scaled = model.predict(X_test_scaled) y_pred = scaler_y.inverse_transform(y_pred_scaled.reshape(-1, 1)).ravel() elif model_type == 'MLP': # 多层感知器回归 model = MLPRegressor(hidden_layer_sizes=(100, 50), max_iter=500, random_state=42) model.fit(X_train_scaled, y_train_scaled) y_pred_scaled = model.predict(X_test_scaled) y_pred = scaler_y.inverse_transform(y_pred_scaled.reshape(-1, 1)).ravel() # 计算评估指标 # 确保y_test和y_pred长度一致 min_len = min(len(y_test), len(y_pred)) if min_len > 0: y_test_trimmed = y_test[:min_len] y_pred_trimmed = y_pred[:min_len] r2 = r2_score(y_test_trimmed, y_pred_trimmed) mse = mean_squared_error(y_test_trimmed, y_pred_trimmed) mae = mean_absolute_error(y_test_trimmed, y_pred_trimmed) rmse = np.sqrt(mse) else: r2 = 0 mse = 0 mae = 0 rmse = 0 # 显示模型性能 metrics_cols = st.columns(2) with metrics_cols[0]: st.metric("R² 得分", f"{r2:.4f}") st.metric("均方误差 (MSE)", f"{mse:.6f}") with metrics_cols[1]: st.metric("平均绝对误差 (MAE)", f"{mae:.6f}") st.metric("均方根误差 (RMSE)", f"{rmse:.6f}") # --- 实际值与预测值对比 --- st.subheader("🔄 实际值与预测值对比") # 创建对比数据 compare_df = pd.DataFrame({ '实际值': y_test_trimmed, '预测值': y_pred_trimmed }) compare_df = compare_df.sort_index() # 创建对比图 fig_compare = go.Figure() fig_compare.add_trace(go.Scatter( x=compare_df.index, y=compare_df['实际值'], name='实际值', mode='lines+markers', line=dict(color='blue', width=2) )) fig_compare.add_trace(go.Scatter( x=compare_df.index, y=compare_df['预测值'], name='预测值', mode='lines+markers', line=dict(color='red', width=2, dash='dash') )) fig_compare.update_layout( title=f'测试集: 实际米重 vs 预测米重 ({model_type})', xaxis=dict(title='时间'), yaxis=dict(title='米重 (Kg/m)'), legend=dict(orientation='h', yanchor='bottom', y=1.02, xanchor='right', x=1), height=400 ) st.plotly_chart(fig_compare, width='stretch') # --- 残差分析 --- st.subheader("📉 残差分析") # 计算残差 residuals = y_test_trimmed - y_pred_trimmed # 创建残差图 fig_residual = go.Figure() fig_residual.add_trace(go.Scatter( x=y_pred, y=residuals, mode='markers', marker=dict(color='green', size=8, opacity=0.6) )) fig_residual.add_shape( type="line", x0=y_pred.min(), y0=0, x1=y_pred.max(), y1=0, line=dict(color="red", width=2, dash="dash") ) fig_residual.update_layout( title='残差图', xaxis=dict(title='预测值'), yaxis=dict(title='残差'), height=400 ) st.plotly_chart(fig_residual, width='stretch') # --- 特征重要性（如果模型支持） --- if model_type in ['RandomForest', 'GradientBoosting']: st.subheader("⚖️ 特征重要性分析") # 计算特征重要性 feature_importance = pd.DataFrame({ '特征': X_train.columns, '重要性': model.feature_importances_ }) feature_importance = feature_importance.sort_values('重要性', ascending=False) # 创建特征重要性图 fig_importance = px.bar( feature_importance, x='特征', y='重要性', title='特征重要性', color='重要性', color_continuous_scale='viridis' ) fig_importance.update_layout( xaxis=dict(tickangle=-45), height=400 ) st.plotly_chart(fig_importance, width='stretch') # --- 模型保存 --- st.subheader("� 模型保存") # 创建模型目录（如果不存在） model_dir = "saved_models" os.makedirs(model_dir, exist_ok=True) # 准备模型信息 model_info = { 'model': model, 'features': feature_columns, 'scaler_X': scaler_X if model_type in ['SVR', 'MLP'] else None, 'scaler_y': scaler_y if model_type in ['SVR', 'MLP'] else None, 'model_type': model_type, 'created_at': datetime.now(), 'r2_score': r2, 'mse': mse, 'mae': mae, 'rmse': rmse, 'use_steady_data': use_steady_data } # 生成模型文件名 model_filename = f"advanced_{model_type.lower()}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.joblib" model_path = os.path.join(model_dir, model_filename) # 保存模型 joblib.dump(model_info, model_path) st.success(f"模型已成功保存: {model_filename}") st.info(f"保存路径: {model_path}") # --- 数据预览 --- st.subheader("🔍 数据预览") st.dataframe(df_analysis.head(20), width='stretch') # --- 导出数据 --- st.subheader("💾 导出数据") # 将数据转换为CSV格式 csv = df_analysis.to_csv(index=False) # 创建下载按钮 st.download_button( label="导出整合后的数据 (CSV)", data=csv, file_name=f"metered_weight_advanced_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv", mime="text/csv", help="点击按钮导出整合后的米重分析数据" ) except Exception as e: st.error(f"模型训练或预测失败: {str(e)}") else: # 提示用户点击开始分析按钮 st.info("请选择时间范围并点击'开始分析'按钮获取数据。")