From 6628f663b636675bcaea316f2deaddf337de480e Mon Sep 17 00:00:00 2001
From: baoshiwei <baoshiwei@shlanbao.cn>
Date: 星期五, 13 三月 2026 10:23:31 +0800
Subject: [PATCH] feat(米重分析): 新增稳态识别和预测功能页面并优化现有模型
---
app/pages/metered_weight_advanced pytorch.py | 873 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 873 insertions(+), 0 deletions(-)
diff --git a/app/pages/metered_weight_advanced pytorch.py b/app/pages/metered_weight_advanced pytorch.py
new file mode 100644
index 0000000..2f35692
--- /dev/null
+++ b/app/pages/metered_weight_advanced pytorch.py
@@ -0,0 +1,873 @@
+import streamlit as st
+import plotly.express as px
+import plotly.graph_objects as go
+import pandas as pd
+import numpy as np
+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
+
+# 灏濊瘯瀵煎叆娣卞害瀛︿範搴�
+use_deep_learning = False
+try:
+
+ import torch
+ import torch.nn as nn
+ import torch.optim as optim
+ use_deep_learning = True
+ # 妫�娴婫PU鏄惁鍙敤
+ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+ st.success(f"浣跨敤璁惧: {device}")
+except ImportError:
+ st.warning("鏈娴嬪埌PyTorch锛屾繁搴﹀涔犳ā鍨嬪皢涓嶅彲鐢ㄣ�傝瀹夎pytorch浠ヤ娇鐢↙STM/GRU妯″瀷銆�")
+
+
+# PyTorch娣卞害瀛︿範妯″瀷瀹氫箟
+class LSTMModel(nn.Module):
+ def __init__(self, input_dim, hidden_dim=64, num_layers=2):
+ super(LSTMModel, self).__init__()
+ self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)
+ self.fc1 = nn.Linear(hidden_dim, 32)
+ self.dropout = nn.Dropout(0.2)
+ self.fc2 = nn.Linear(32, 1)
+
+ def forward(self, x):
+ out, _ = self.lstm(x)
+ out = out[:, -1, :]
+ out = torch.relu(self.fc1(out))
+ out = self.dropout(out)
+ out = self.fc2(out)
+ return out
+
+class GRUModel(nn.Module):
+ def __init__(self, input_dim, hidden_dim=64, num_layers=2):
+ super(GRUModel, self).__init__()
+ self.gru = nn.GRU(input_dim, hidden_dim, num_layers, batch_first=True)
+ self.fc1 = nn.Linear(hidden_dim, 32)
+ self.dropout = nn.Dropout(0.2)
+ self.fc2 = nn.Linear(32, 1)
+
+ def forward(self, x):
+ out, _ = self.gru(x)
+ out = out[:, -1, :]
+ out = torch.relu(self.fc1(out))
+ out = self.dropout(out)
+ out = self.fc2(out)
+ return out
+
+class BiLSTMModel(nn.Module):
+ def __init__(self, input_dim, hidden_dim=64, num_layers=2):
+ super(BiLSTMModel, self).__init__()
+ self.bilstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True, bidirectional=True)
+ self.fc1 = nn.Linear(hidden_dim * 2, 32)
+ self.dropout = nn.Dropout(0.2)
+ self.fc2 = nn.Linear(32, 1)
+
+ def forward(self, x):
+ out, _ = self.bilstm(x)
+ out = out[:, -1, :]
+ out = torch.relu(self.fc1(out))
+ out = self.dropout(out)
+ out = self.fc2(out)
+ return out
+
+def show_metered_weight_advanced():
+ # 鍒濆鍖栨湇鍔�
+ extruder_service = ExtruderService()
+ main_process_service = MainProcessService()
+
+ # 椤甸潰鏍囬
+ st.title("绫抽噸楂樼骇棰勬祴鍒嗘瀽")
+
+ # 鍒濆鍖栦細璇濈姸鎬�
+ 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'] = "鏈�杩�7澶�"
+ 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
+
+ # 榛樿鐗瑰緛鍒楄〃锛堜笉鍐嶅厑璁哥敤鎴烽�夋嫨锛�
+ default_features = ['铻烘潌杞��', '鏈哄ご鍘嬪姏', '娴佺▼涓婚��', '铻烘潌娓╁害',
+ '鍚庢満绛掓俯搴�', '鍓嶆満绛掓俯搴�', '鏈哄ご娓╁害']
+
+ # 瀹氫箟鍥炶皟鍑芥暟
+ def update_dates(qs):
+ st.session_state['ma_quick_select'] = qs
+ today = datetime.now().date()
+ if qs == "浠婂ぉ":
+ st.session_state['ma_start_date'] = today
+ st.session_state['ma_end_date'] = today
+ elif qs == "鏈�杩�3澶�":
+ st.session_state['ma_start_date'] = today - timedelta(days=3)
+ st.session_state['ma_end_date'] = today
+ elif qs == "鏈�杩�7澶�":
+ st.session_state['ma_start_date'] = today - timedelta(days=7)
+ st.session_state['ma_end_date'] = today
+ elif qs == "鏈�杩�30澶�":
+ 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'] = "鑷畾涔�"
+
+ # 鏌ヨ鏉′欢鍖哄煙
+ with st.expander("馃攳 鏌ヨ閰嶇疆", expanded=True):
+ # 娣诲姞鑷畾涔� CSS 瀹炵幇鍝嶅簲寮忔崲琛�
+ st.markdown("""
+ <style>
+ /* 寮哄埗鍒楀鍣ㄦ崲琛� */
+ [data-testid="stExpander"] [data-testid="column"] {
+ flex: 1 1 120px !important;
+ min-width: 120px !important;
+ }
+ /* 閽堝鏃ユ湡杈撳叆妗嗗垪绋嶅井鍔犲涓�鐐� */
+ @media (min-width: 768px) {
+ [data-testid="stExpander"] [data-testid="column"]:nth-child(6),
+ [data-testid="stExpander"] [data-testid="column"]:nth-child(7) {
+ flex: 2 1 180px !important;
+ min-width: 180px !important;
+ }
+ }
+ </style>
+ """, unsafe_allow_html=True)
+
+ # 鍒涘缓甯冨眬
+ cols = st.columns([1, 1, 1, 1, 1, 1.5, 1.5, 1])
+
+ options = ["浠婂ぉ", "鏈�杩�3澶�", "鏈�杩�7澶�", "鏈�杩�30澶�", "鑷畾涔�"]
+ for i, option in enumerate(options):
+ with cols[i]:
+ # 鏍规嵁褰撳墠閫夋嫨鐘舵�佸喅瀹氭寜閽被鍨�
+ 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(
+ "寮�濮嬫棩鏈�",
+ label_visibility="collapsed",
+ key="ma_start_date",
+ on_change=on_date_change
+ )
+
+ with cols[6]:
+ end_date = st.date_input(
+ "缁撴潫鏃ユ湡",
+ label_visibility="collapsed",
+ key="ma_end_date",
+ on_change=on_date_change
+ )
+
+ with cols[7]:
+ query_button = st.button("馃殌 寮�濮嬪垎鏋�", key="ma_query", width='stretch')
+
+ # 妯″瀷閰嶇疆
+ st.markdown("---")
+ st.write("馃 **妯″瀷閰嶇疆**")
+ model_cols = st.columns(2)
+
+ with model_cols[0]:
+ # 妯″瀷绫诲瀷閫夋嫨
+ model_options = ['RandomForest', 'GradientBoosting', 'SVR', 'MLP']
+ if use_deep_learning:
+ model_options.extend(['LSTM', 'GRU', 'BiLSTM'])
+
+ model_type = st.selectbox(
+ "妯″瀷绫诲瀷",
+ options=model_options,
+ key="ma_model_type",
+ help="閫夋嫨鐢ㄤ簬棰勬祴鐨勬ā鍨嬬被鍨�"
+ )
+
+ with model_cols[1]:
+ # 搴忓垪闀垮害锛堜粎閫傜敤浜庢繁搴﹀涔犳ā鍨嬶級
+ if model_type in ['LSTM', 'GRU', 'BiLSTM']:
+ sequence_length = st.slider(
+ "搴忓垪闀垮害",
+ min_value=5,
+ max_value=30,
+ value=st.session_state['ma_sequence_length'],
+ step=1,
+ help="鐢ㄤ簬娣卞害瀛︿範妯″瀷鐨勬椂闂村簭鍒楅暱搴�",
+ key="ma_sequence_length"
+ )
+ else:
+ st.session_state['ma_sequence_length'] = 10
+ st.write("搴忓垪闀垮害: 10 (榛樿锛屼粎閫傜敤浜庢繁搴﹀涔犳ā鍨�)")
+
+ # 杞崲涓篸atetime瀵硅薄
+ start_dt = datetime.combine(start_date, datetime.min.time())
+ end_dt = datetime.combine(end_date, datetime.max.time())
+
+ # 鏌ヨ澶勭悊
+ if query_button:
+ with st.spinner("姝ｅ湪鑾峰彇鏁版嵁..."):
+ # 1. 鑾峰彇瀹屾暣鐨勬尋鍑烘満鏁版嵁
+ df_extruder_full = extruder_service.get_extruder_data(start_dt, end_dt)
+
+ # 2. 鑾峰彇涓绘祦绋嬫帶鍒舵暟鎹�
+ 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)
+
+ # 妫�鏌ユ槸鍚︽湁鏁版嵁
+ 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("鎵�閫夋椂闂存鍐呮湭鎵惧埌浠讳綍鏁版嵁锛岃灏濊瘯璋冩暣鏌ヨ鏉′欢銆�")
+ # 娓呴櫎缂撳瓨鏁版嵁
+ 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
+
+ # 缂撳瓨鏁版嵁鍒颁細璇濈姸鎬�
+ 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
+
+ # 鏁版嵁澶勭悊鍜屽垎鏋�
+ if all(key in st.session_state for key in ['cached_extruder_full', 'cached_main_speed', 'cached_temp']):
+ with st.spinner("姝ｅ湪鍒嗘瀽鏁版嵁..."):
+ # 鑾峰彇缂撳瓨鏁版嵁
+ 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']
+
+
+
+ # 妫�鏌ユ槸鍚︽湁鏁版嵁
+ 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("鎵�閫夋椂闂存鍐呮湭鎵惧埌浠讳綍鏁版嵁锛岃灏濊瘯璋冩暣鏌ヨ鏉′欢銆�")
+ return
+
+ # 鏁版嵁鏁村悎涓庨澶勭悊
+ def integrate_data(df_extruder_full, df_main_speed, df_temp):
+ # 纭繚鎸ゅ嚭鏈烘暟鎹瓨鍦�
+ if df_extruder_full is None or df_extruder_full.empty:
+ return None
+
+ # 鍒涘缓鍙寘鍚背閲嶅拰鏃堕棿鐨勪富鏁版嵁闆�
+ df_merged = df_extruder_full[['time', 'metered_weight', 'screw_speed_actual', 'head_pressure']].copy()
+
+
+ # 鏁村悎涓绘祦绋嬫暟鎹�
+ 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')
+ )
+
+ # 鏁村悎娓╁害鏁版嵁
+ 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')
+ )
+
+ # 閲嶅懡鍚嶅垪浠ユ彁楂樺彲璇绘��
+ df_merged.rename(columns={
+ 'screw_speed_actual': '铻烘潌杞��',
+ 'head_pressure': '鏈哄ご鍘嬪姏',
+ 'process_main_speed': '娴佺▼涓婚��',
+ 'nakata_extruder_screw_display_temp': '铻烘潌娓╁害',
+ 'nakata_extruder_rear_barrel_display_temp': '鍚庢満绛掓俯搴�',
+ 'nakata_extruder_front_barrel_display_temp': '鍓嶆満绛掓俯搴�',
+ 'nakata_extruder_head_display_temp': '鏈哄ご娓╁害'
+ }, inplace=True)
+
+ # 娓呯悊鏁版嵁
+ df_merged.dropna(subset=['metered_weight'], inplace=True)
+
+ return df_merged
+
+ # 鎵ц鏁版嵁鏁村悎
+ df_analysis = integrate_data(df_extruder_full, df_main_speed, df_temp)
+
+ if df_analysis is None or df_analysis.empty:
+ st.warning("鏁版嵁鏁村悎澶辫触锛岃妫�鏌ユ暟鎹川閲忔垨璋冩暣鏃堕棿鑼冨洿銆�")
+ return
+
+ # 閲嶅懡鍚嶇背閲嶅垪
+ df_analysis.rename(columns={'metered_weight': '绫抽噸'}, inplace=True)
+
+ # --- 鍘熷鏁版嵁瓒嬪娍鍥� ---
+ st.subheader("馃搱 鍘熷鏁版嵁瓒嬪娍鍥�")
+
+ # 鍒涘缓瓒嬪娍鍥�
+ fig_trend = go.Figure()
+
+ # 娣诲姞绫抽噸鏁版嵁
+ 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='绫抽噸 (Kg/m)',
+ mode='lines',
+ line=dict(color='blue', width=2)
+ ))
+
+ # 娣诲姞铻烘潌杞��
+ fig_trend.add_trace(go.Scatter(
+ x=df_extruder_full['time'],
+ y=df_extruder_full['screw_speed_actual'],
+ name='铻烘潌杞�� (RPM)',
+ mode='lines',
+ line=dict(color='green', width=1.5),
+ yaxis='y2'
+ ))
+
+ # 娣诲姞鏈哄ご鍘嬪姏
+ fig_trend.add_trace(go.Scatter(
+ x=df_extruder_full['time'],
+ y=df_extruder_full['head_pressure'],
+ name='鏈哄ご鍘嬪姏',
+ mode='lines',
+ line=dict(color='orange', width=1.5),
+ yaxis='y3'
+ ))
+
+ # 娣诲姞娴佺▼涓婚��
+ 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='娴佺▼涓婚�� (M/Min)',
+ mode='lines',
+ line=dict(color='red', width=1.5),
+ yaxis='y4'
+ ))
+
+ # 娣诲姞娓╁害鏁版嵁
+ if df_temp is not None and not df_temp.empty:
+ # 铻烘潌娓╁害
+ fig_trend.add_trace(go.Scatter(
+ x=df_temp['time'],
+ y=df_temp['nakata_extruder_screw_display_temp'],
+ name='铻烘潌娓╁害 (掳C)',
+ mode='lines',
+ line=dict(color='purple', width=1),
+ yaxis='y5'
+ ))
+
+ # 閰嶇疆瓒嬪娍鍥惧竷灞�
+ fig_trend.update_layout(
+ title='鍘熷鏁版嵁瓒嬪娍',
+ xaxis=dict(
+ title='鏃堕棿',
+ rangeslider=dict(visible=True),
+ type='date'
+ ),
+ yaxis=dict(
+ title='绫抽噸 (Kg/m)',
+ title_font=dict(color='blue'),
+ tickfont=dict(color='blue')
+ ),
+ yaxis2=dict(
+ title='铻烘潌杞�� (RPM)',
+ title_font=dict(color='green'),
+ tickfont=dict(color='green'),
+ overlaying='y',
+ side='right'
+ ),
+ yaxis3=dict(
+ title='鏈哄ご鍘嬪姏',
+ title_font=dict(color='orange'),
+ tickfont=dict(color='orange'),
+ overlaying='y',
+ side='right',
+ anchor='free',
+ position=0.85
+ ),
+ yaxis4=dict(
+ title='娴佺▼涓婚�� (M/Min)',
+ title_font=dict(color='red'),
+ tickfont=dict(color='red'),
+ overlaying='y',
+ side='right',
+ anchor='free',
+ position=0.75
+ ),
+ yaxis5=dict(
+ title='娓╁害 (掳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'
+ )
+
+ # 鏄剧ず瓒嬪娍鍥�
+ st.plotly_chart(fig_trend, width='stretch', config={'scrollZoom': True})
+
+ # --- 楂樼骇棰勬祴鍒嗘瀽 ---
+ st.subheader("馃搳 楂樼骇棰勬祴鍒嗘瀽")
+
+ # 妫�鏌ユ墍鏈夐粯璁ょ壒寰佹槸鍚﹀湪鏁版嵁涓�
+ missing_features = [f for f in default_features if f not in df_analysis.columns]
+ if missing_features:
+ st.warning(f"鏁版嵁涓己灏戜互涓嬬壒寰�: {', '.join(missing_features)}")
+ else:
+ # 鍑嗗鏁版嵁
+ X = df_analysis[default_features]
+ y = df_analysis['绫抽噸']
+
+ # 娓呯悊鏁版嵁涓殑NaN鍊�
+ combined = pd.concat([X, y], axis=1)
+ combined_clean = combined.dropna()
+
+ # 妫�鏌ユ竻鐞嗗悗鐨勬暟鎹噺
+ if len(combined_clean) < 30:
+ st.warning("鏁版嵁閲忎笉瓒筹紝鏃犳硶杩涜鏈夋晥鐨勯娴嬪垎鏋�")
+ else:
+ # 閲嶆柊鍒嗙X鍜寉
+ X_clean = combined_clean[default_features]
+ y_clean = combined_clean['绫抽噸']
+
+ # 鐗瑰緛宸ョ▼锛氭坊鍔犳椂闂寸浉鍏崇壒寰�
+ # 纭繚浣跨敤鏃堕棿鍒椾綔涓虹储寮�
+ if 'time' in combined_clean.columns:
+ # 灏唗ime鍒楄缃负绱㈠紩
+ combined_clean = combined_clean.set_index('time')
+
+ # 鍒涘缓鏃堕棿鐗瑰緛
+ time_features = pd.DataFrame(index=combined_clean.index)
+ time_features['hour'] = combined_clean.index.hour
+ time_features['minute'] = combined_clean.index.minute
+ time_features['second'] = combined_clean.index.second
+ time_features['time_of_day'] = time_features['hour'] * 3600 + time_features['minute'] * 60 + time_features['second']
+ else:
+ # 濡傛灉娌℃湁time鍒楋紝鍒涘缓绌虹殑鏃堕棿鐗瑰緛
+ time_features = pd.DataFrame(index=combined_clean.index)
+ time_features['hour'] = 0
+ time_features['minute'] = 0
+ time_features['second'] = 0
+ time_features['time_of_day'] = 0
+
+ # 娣诲姞婊炲悗鐗瑰緛
+ for feature in default_features:
+ for lag in [1, 2, 3]:
+ time_features[f'{feature}_lag{lag}'] = X_clean[feature].shift(lag)
+ time_features[f'{feature}_diff{lag}'] = X_clean[feature].diff(lag)
+
+ # 娣诲姞婊氬姩缁熻鐗瑰緛
+ for feature in default_features:
+ time_features[f'{feature}_rolling_mean'] = X_clean[feature].rolling(window=5).mean()
+ time_features[f'{feature}_rolling_std'] = X_clean[feature].rolling(window=5).std()
+ time_features[f'{feature}_rolling_min'] = X_clean[feature].rolling(window=5).min()
+ time_features[f'{feature}_rolling_max'] = X_clean[feature].rolling(window=5).max()
+
+ # 娓呯悊婊炲悗鐗瑰緛鍜屾粴鍔ㄧ粺璁＄壒寰佷骇鐢熺殑NaN鍊�
+ time_features.dropna(inplace=True)
+
+ # 瀵归綈X鍜寉
+ common_index = time_features.index.intersection(y_clean.index)
+ X_final = pd.concat([X_clean.loc[common_index], time_features.loc[common_index]], axis=1)
+ y_final = y_clean.loc[common_index]
+
+ # 妫�鏌ユ渶缁堟暟鎹噺
+ if len(X_final) < 20:
+ st.warning("鐗瑰緛宸ョ▼鍚庢暟鎹噺涓嶈冻锛屾棤娉曡繘琛屾湁鏁堢殑棰勬祴鍒嗘瀽")
+ else:
+ # 鍒嗗壊璁粌闆嗗拰娴嬭瘯闆�
+ X_train, X_test, y_train, y_test = train_test_split(X_final, y_final, test_size=0.2, random_state=42)
+
+ # 鏁版嵁鏍囧噯鍖�
+ 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()
+
+ # 妯″瀷璁粌
+ model = None
+ y_pred = None
+
+ try:
+ if model_type == 'RandomForest':
+ # 闅忔満妫灄鍥炲綊
+ model = RandomForestRegressor(n_estimators=100, random_state=42)
+ model.fit(X_train, y_train)
+ y_pred = model.predict(X_test)
+
+ elif model_type == 'GradientBoosting':
+ # 姊害鎻愬崌鍥炲綊
+ model = GradientBoostingRegressor(n_estimators=100, random_state=42)
+ model.fit(X_train, y_train)
+ y_pred = model.predict(X_test)
+
+ elif model_type == 'SVR':
+ # 鏀寔鍚戦噺鍥炲綊
+ 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':
+ # 澶氬眰鎰熺煡鍣ㄥ洖褰�
+ 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()
+
+ elif use_deep_learning and model_type in ['LSTM', 'GRU', 'BiLSTM']:
+ # 鍑嗗鏃堕棿搴忓垪鏁版嵁
+ sequence_length = st.session_state['ma_sequence_length']
+
+ def create_sequences(X, y, seq_length):
+ X_seq = []
+ y_seq = []
+ # 纭繚X鍜寉鐨勯暱搴︿竴鑷�
+ min_len = min(len(X), len(y))
+ # 纭繚X鍜寉鐨勯暱搴﹁嚦灏戜负seq_length + 1
+ if min_len <= seq_length:
+ return np.array([]), np.array([])
+ # 鎴柇X鍜寉鍒扮浉鍚岄暱搴�
+ X_trimmed = X[:min_len]
+ y_trimmed = y[:min_len]
+ # 鍒涘缓搴忓垪
+ for i in range(len(X_trimmed) - seq_length):
+ X_seq.append(X_trimmed[i:i+seq_length])
+ y_seq.append(y_trimmed[i+seq_length])
+ return np.array(X_seq), np.array(y_seq)
+
+ # 涓烘繁搴﹀涔犳ā鍨嬪垱寤哄簭鍒�
+ X_train_seq, y_train_seq = create_sequences(X_train_scaled, y_train_scaled, sequence_length)
+ X_test_seq, y_test_seq = create_sequences(X_test_scaled, y_test_scaled, sequence_length)
+
+ # 妫�鏌ュ垱寤虹殑搴忓垪鏄惁涓虹┖
+ if len(X_train_seq) == 0 or len(y_train_seq) == 0:
+ st.warning(f"鏁版嵁閲忎笉瓒筹紝鏃犳硶鍒涘缓鏈夋晥鐨凩STM搴忓垪銆傞渶瑕佽嚦灏� {sequence_length + 1} 涓牱鏈紝褰撳墠鍙湁 {min(len(X_train_scaled), len(y_train_scaled))} 涓牱鏈��")
+ # 浣跨敤闅忔満妫灄浣滀负澶囬�夋ā鍨�
+ model = RandomForestRegressor(n_estimators=100, random_state=42)
+ model.fit(X_train, y_train)
+ y_pred = model.predict(X_test)
+ else:
+ # 杞崲涓篜yTorch寮犻噺骞剁Щ鍔ㄥ埌璁惧
+ X_train_tensor = torch.tensor(X_train_seq, dtype=torch.float32).to(device)
+ y_train_tensor = torch.tensor(y_train_seq, dtype=torch.float32).unsqueeze(1).to(device)
+ X_test_tensor = torch.tensor(X_test_seq, dtype=torch.float32).to(device)
+ y_test_tensor = torch.tensor(y_test_seq, dtype=torch.float32).unsqueeze(1).to(device)
+
+ # 鏋勫缓PyTorch妯″瀷骞剁Щ鍔ㄥ埌璁惧
+ input_dim = X_train_scaled.shape[1]
+
+ if model_type == 'LSTM':
+ deep_model = LSTMModel(input_dim).to(device)
+ elif model_type == 'GRU':
+ deep_model = GRUModel(input_dim).to(device)
+ elif model_type == 'BiLSTM':
+ deep_model = BiLSTMModel(input_dim).to(device)
+
+ # 瀹氫箟鎹熷け鍑芥暟鍜屼紭鍖栧櫒
+ criterion = nn.MSELoss()
+ optimizer = optim.Adam(deep_model.parameters(), lr=0.001)
+
+ # 鏄剧ず浣跨敤鐨勮澶�
+ st.info(f"浣跨敤璁惧: {device}")
+
+ # 璁粌妯″瀷
+ num_epochs = 50
+ batch_size = 32
+
+ for epoch in range(num_epochs):
+ deep_model.train()
+ optimizer.zero_grad()
+
+ # 鍓嶅悜浼犳挱
+ outputs = deep_model(X_train_tensor)
+ loss = criterion(outputs, y_train_tensor)
+
+ # 鍙嶅悜浼犳挱鍜屼紭鍖�
+ loss.backward()
+ optimizer.step()
+
+ # 棰勬祴
+ deep_model.eval()
+ with torch.no_grad():
+ y_pred_scaled_tensor = deep_model(X_test_tensor)
+ y_pred_scaled = y_pred_scaled_tensor.numpy().ravel()
+ y_pred = scaler_y.inverse_transform(y_pred_scaled.reshape(-1, 1)).ravel()
+
+ # 灏唝_test_seq杞崲鍥炲師濮嬪昂搴�
+ y_test_actual = scaler_y.inverse_transform(y_test_seq.reshape(-1, 1)).ravel()
+
+ # 淇濆瓨妯″瀷
+ model = deep_model
+
+ # 璁＄畻璇勪及鎸囨爣
+ if model_type in ['LSTM', 'GRU', 'BiLSTM']:
+ # 浣跨敤杞崲鍚庣殑y_test_seq浣滀负鐪熷疄鍊�
+ r2 = r2_score(y_test_actual, y_pred)
+ mse = mean_squared_error(y_test_actual, y_pred)
+ mae = mean_absolute_error(y_test_actual, y_pred)
+ rmse = np.sqrt(mse)
+ else:
+ # 浣跨敤鍘熷鐨剏_test浣滀负鐪熷疄鍊�
+ r2 = r2_score(y_test, y_pred)
+ mse = mean_squared_error(y_test, y_pred)
+ mae = mean_absolute_error(y_test, y_pred)
+ rmse = np.sqrt(mse)
+
+ # 鏄剧ず妯″瀷鎬ц兘
+ metrics_cols = st.columns(2)
+ with metrics_cols[0]:
+ st.metric("R虏 寰楀垎", f"{r2:.4f}")
+ st.metric("鍧囨柟璇樊 (MSE)", f"{mse:.6f}")
+ with metrics_cols[1]:
+ st.metric("骞冲潎缁濆璇樊 (MAE)", f"{mae:.6f}")
+ st.metric("鍧囨柟鏍硅宸� (RMSE)", f"{rmse:.6f}")
+
+ # --- 瀹為檯鍊间笌棰勬祴鍊煎姣� ---
+ st.subheader("馃攧 瀹為檯鍊间笌棰勬祴鍊煎姣�")
+
+ # 鍒涘缓瀵规瘮鏁版嵁
+ if model_type in ['LSTM', 'GRU', 'BiLSTM']:
+ # 浣跨敤杞崲鍚庣殑y_test_actual
+ compare_df = pd.DataFrame({
+ '瀹為檯鍊�': y_test_actual,
+ '棰勬祴鍊�': y_pred
+ })
+ else:
+ # 浣跨敤鍘熷鐨剏_test
+ compare_df = pd.DataFrame({
+ '瀹為檯鍊�': y_test,
+ '棰勬祴鍊�': y_pred
+ })
+ compare_df = compare_df.sort_index()
+
+ # 鍒涘缓瀵规瘮鍥�
+ fig_compare = go.Figure()
+ fig_compare.add_trace(go.Scatter(
+ x=compare_df.index,
+ y=compare_df['瀹為檯鍊�'],
+ name='瀹為檯鍊�',
+ mode='lines+markers',
+ line=dict(color='blue', width=2)
+ ))
+ fig_compare.add_trace(go.Scatter(
+ x=compare_df.index,
+ y=compare_df['棰勬祴鍊�'],
+ name='棰勬祴鍊�',
+ mode='lines+markers',
+ line=dict(color='red', width=2, dash='dash')
+ ))
+ fig_compare.update_layout(
+ title=f'娴嬭瘯闆�: 瀹為檯绫抽噸 vs 棰勬祴绫抽噸 ({model_type})',
+ xaxis=dict(title='鏃堕棿'),
+ yaxis=dict(title='绫抽噸 (Kg/m)'),
+ legend=dict(orientation='h', yanchor='bottom', y=1.02, xanchor='right', x=1),
+ height=400
+ )
+ st.plotly_chart(fig_compare, width='stretch')
+
+ # --- 娈嬪樊鍒嗘瀽 ---
+ st.subheader("馃搲 娈嬪樊鍒嗘瀽")
+
+ # 璁＄畻娈嬪樊
+ if model_type in ['LSTM', 'GRU', 'BiLSTM']:
+ # 浣跨敤杞崲鍚庣殑y_test_actual
+ residuals = y_test_actual - y_pred
+ else:
+ # 浣跨敤鍘熷鐨剏_test
+ residuals = y_test - y_pred
+
+ # 鍒涘缓娈嬪樊鍥�
+ 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='娈嬪樊鍥�',
+ xaxis=dict(title='棰勬祴鍊�'),
+ yaxis=dict(title='娈嬪樊'),
+ height=400
+ )
+ st.plotly_chart(fig_residual, width='stretch')
+
+ # --- 鐗瑰緛閲嶈鎬э紙濡傛灉妯″瀷鏀寔锛� ---
+ if model_type in ['RandomForest', 'GradientBoosting']:
+ st.subheader("鈿栵笍 鐗瑰緛閲嶈鎬у垎鏋�")
+
+ # 璁＄畻鐗瑰緛閲嶈鎬�
+ feature_importance = pd.DataFrame({
+ '鐗瑰緛': X_train.columns,
+ '閲嶈鎬�': model.feature_importances_
+ })
+ feature_importance = feature_importance.sort_values('閲嶈鎬�', ascending=False)
+
+ # 鍒涘缓鐗瑰緛閲嶈鎬у浘
+ fig_importance = px.bar(
+ feature_importance,
+ x='鐗瑰緛',
+ y='閲嶈鎬�',
+ title='鐗瑰緛閲嶈鎬�',
+ color='閲嶈鎬�',
+ color_continuous_scale='viridis'
+ )
+ fig_importance.update_layout(
+ xaxis=dict(tickangle=-45),
+ height=400
+ )
+ st.plotly_chart(fig_importance, width='stretch')
+
+ # --- 棰勬祴鍔熻兘 ---
+ st.subheader("馃敭 绫抽噸棰勬祴")
+
+ # 鍒涘缓棰勬祴琛ㄥ崟
+ st.write("杈撳叆鐗瑰緛鍊艰繘琛岀背閲嶉娴�:")
+ predict_cols = st.columns(2)
+ input_features = {}
+
+ for i, feature in enumerate(default_features):
+ with predict_cols[i % 2]:
+ # 鑾峰彇鐗瑰緛鐨勭粺璁′俊鎭�
+ min_val = X_clean[feature].min()
+ max_val = X_clean[feature].max()
+ mean_val = X_clean[feature].mean()
+
+ input_features[feature] = st.number_input(
+ f"{feature}",
+ key=f"ma_pred_{feature}",
+ value=float(mean_val),
+ min_value=float(min_val),
+ max_value=float(max_val),
+ step=0.1
+ )
+
+ if st.button("棰勬祴绫抽噸"):
+ # 鍑嗗棰勬祴鏁版嵁
+ input_df = pd.DataFrame([input_features])
+
+ # 娣诲姞鏃堕棿鐗瑰緛锛堜娇鐢ㄥ綋鍓嶆椂闂达級
+ current_time = datetime.now()
+ time_features_input = pd.DataFrame({
+ 'hour': [current_time.hour],
+ 'minute': [current_time.minute],
+ 'second': [current_time.second],
+ 'time_of_day': [current_time.hour * 3600 + current_time.minute * 60 + current_time.second]
+ })
+
+ # 娣诲姞婊炲悗鐗瑰緛锛堜娇鐢ㄨ緭鍏ュ�间綔涓烘浛浠ｏ級
+ for feature in default_features:
+ for lag in [1, 2, 3]:
+ time_features_input[f'{feature}_lag{lag}'] = input_features[feature]
+ time_features_input[f'{feature}_diff{lag}'] = 0.0
+
+ # 娣诲姞婊氬姩缁熻鐗瑰緛锛堜娇鐢ㄨ緭鍏ュ�间綔涓烘浛浠ｏ級
+ for feature in default_features:
+ time_features_input[f'{feature}_rolling_mean'] = input_features[feature]
+ time_features_input[f'{feature}_rolling_std'] = 0.0
+ time_features_input[f'{feature}_rolling_min'] = input_features[feature]
+ time_features_input[f'{feature}_rolling_max'] = input_features[feature]
+
+ # 鍚堝苟鐗瑰緛
+ input_combined = pd.concat([input_df, time_features_input], axis=1)
+
+ # 棰勬祴
+ if model_type in ['SVR', 'MLP']:
+ input_scaled = scaler_X.transform(input_combined)
+ prediction_scaled = model.predict(input_scaled)
+ predicted_weight = scaler_y.inverse_transform(prediction_scaled.reshape(-1, 1)).ravel()[0]
+ elif use_deep_learning and model_type in ['LSTM', 'GRU', 'BiLSTM']:
+ # 涓烘繁搴﹀涔犳ā鍨嬪垱寤哄簭鍒�
+ input_scaled = scaler_X.transform(input_combined)
+ # 閲嶅杈撳叆浠ュ垱寤哄簭鍒�
+ input_seq = np.tile(input_scaled, (sequence_length, 1)).reshape(1, sequence_length, -1)
+ # 杞崲涓篜yTorch寮犻噺骞剁Щ鍔ㄥ埌璁惧
+ input_tensor = torch.tensor(input_seq, dtype=torch.float32).to(device)
+ # 棰勬祴
+ model.eval()
+ with torch.no_grad():
+ prediction_scaled_tensor = model(input_tensor)
+ prediction_scaled = prediction_scaled_tensor.cpu().numpy().ravel()[0]
+ predicted_weight = scaler_y.inverse_transform(prediction_scaled.reshape(-1, 1)).ravel()[0]
+ else:
+ predicted_weight = model.predict(input_combined)[0]
+
+ # 鏄剧ず棰勬祴缁撴灉
+ st.success(f"棰勬祴绫抽噸: {predicted_weight:.4f} Kg/m")
+
+ # --- 鏁版嵁棰勮 ---
+ st.subheader("馃攳 鏁版嵁棰勮")
+ st.dataframe(df_analysis.head(20), width='stretch')
+
+ # --- 瀵煎嚭鏁版嵁 ---
+ st.subheader("馃捑 瀵煎嚭鏁版嵁")
+ # 灏嗘暟鎹浆鎹负CSV鏍煎紡
+ csv = df_analysis.to_csv(index=False)
+ # 鍒涘缓涓嬭浇鎸夐挳
+ st.download_button(
+ label="瀵煎嚭鏁村悎鍚庣殑鏁版嵁 (CSV)",
+ data=csv,
+ file_name=f"metered_weight_advanced_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
+ mime="text/csv",
+ help="鐐瑰嚮鎸夐挳瀵煎嚭鏁村悎鍚庣殑绫抽噸鍒嗘瀽鏁版嵁"
+ )
+ except Exception as e:
+ st.error(f"妯″瀷璁粌鎴栭娴嬪け璐�: {str(e)}")
+
+ else:
+ # 鎻愮ず鐢ㄦ埛鐐瑰嚮寮�濮嬪垎鏋愭寜閽�
+ st.info("璇烽�夋嫨鏃堕棿鑼冨洿骞剁偣鍑�'寮�濮嬪垎鏋�'鎸夐挳鑾峰彇鏁版嵁銆�")
--
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