fix: 8个严重bug修复 - 预测引擎从空壳恢复为全功能

修复清单:
1. jyotish_engine.py: 缺少 List 类型导入 (NameError崩溃)
2. cmd_predict: 传给EventPredictionModel的ascendant是str不是dict
3. cmd_predict: 未传入dasha/congregation/vivah_saham/chara_dasha数据
4. Dasha timeline: MD的is_current字段未设置
5. event_prediction_model: status检查不兼容中英混合格式
6. event_prediction_model: Dasha数据格式不匹配(current_dasha vs current_mahadasha)
7. event_prediction_model: Chara Dasha key名不匹配(dasha_sequence vs dasha_list)
8. cmd_predict: 序列化缺少confidence/dasha_signals/transit_signals字段

修复前: predict命令直接fallback到空壳,所有事件概率30%,0信号
修复后: marriage=43%(1静态+1Dasha), career=54%(3Dasha)

scripts/event_prediction_model.py

@@ -252,11 +252,13 @@ class EventPredictionModel:
                 if ph == h and pn in target_karakas:
                     pd = self.chart.get('planets', {}).get(pn, {})
                     status = pd.get('status', '')
-                    if status == 'exalted':
+                    # 支持"擢升(Exalted)"、"落陷(Debilitated)"、"入庙(Own Sign)"等中英混合格式
+                    status_lower = status.lower() if status else ''
+                    if 'exalted' in status_lower or '擢升' in status:
                         result['signals'].append(f'Karaka {pn}在{h}宫擢升')
-                    elif status == 'own_sign':
+                    elif 'own' in status_lower or '入庙' in status:
                         result['signals'].append(f'Karaka {pn}在{h}宫入庙')
-                    elif status == 'debilitated':
+                    elif 'debilitated' in status_lower or '落陷' in status:
                         result['signals'].append(f'Karaka {pn}在{h}宫落陷(负面)')
 
         # 2. 行星聚集分析（使用 congregation_data）
@@ -309,8 +311,24 @@ class EventPredictionModel:
 
         # ── 2a. Vimshottari Dasha ──
         if self.dasha:
-            # 当前 Mahadasha
+            # 适配 full-reading 输出格式：current_dasha.lord + current_dasha.antardasha[]
-            md_lord = self.dasha.get('current_mahadasha', {}).get('lord', '')
+            md_lord = ''
+            ad_lord = ''
+
+            # 方式1：current_dasha 格式（full-reading输出）
+            current_md = self.dasha.get('current_dasha')
+            if current_md and isinstance(current_md, dict):
+                md_lord = current_md.get('lord', '')
+                # 从 antardasha 列表中找 is_current=True 的
+                for ad in current_md.get('antardasha', []):
+                    if ad.get('is_current'):
+                        ad_lord = ad.get('lord', '')
+                        break
+            else:
+                # 方式2：current_mahadasha / current_antardasha 格式
+                md_lord = self.dasha.get('current_mahadasha', {}).get('lord', '')
+                ad_lord = self.dasha.get('current_antardasha', {}).get('lord', '')
+
             if md_lord:
                 md_house = self.planet_houses.get(md_lord, 0)
                 # MD 主星是否关联目标宫位
@@ -326,8 +344,6 @@ class EventPredictionModel:
                     if self.house_lords.get(h) == md_lord:
                         signals.append(f'当前MD {md_lord}是{h}宫主(目标宫)')
 
-            # 当前 Antardasha
-            ad_lord = self.dasha.get('current_antardasha', {}).get('lord', '')
             if ad_lord:
                 ad_house = self.planet_houses.get(ad_lord, 0)
                 if ad_house in target_houses:
@@ -339,28 +355,30 @@ class EventPredictionModel:
 
                 # MD+AD 组合信号（高权重）
                 if md_lord and ad_lord:
-                    if ad_house in target_houses and md_house in target_houses:
+                    md_house2 = self.planet_houses.get(md_lord, 0)
+                    if ad_house in target_houses and md_house2 in target_houses:
                         signals.append(f'★ MD+AD双激活目标宫位({md_lord}+{ad_lord})')
 
         # ── 2b. Chara Dasha (Jaimini) ──
         if self.chara_dasha:
-            cd_list = self.chara_dasha.get('dasha_list', [])
+            # 适配实际格式：dasha_sequence[] 或 dasha_list[]
+            cd_list = self.chara_dasha.get('dasha_sequence') or self.chara_dasha.get('dasha_list', [])
             if cd_list:
-                # 当前 Chara Mahadasha
+                # 当前 Chara Mahadasha（第一个条目）
                 current_cd = cd_list[0] if cd_list else {}
                 cd_sign = current_cd.get('sign', '')
-                cd_lord = SIGN_LORDS.get(cd_sign, '')
+                cd_lord = current_cd.get('lord', '') or SIGN_LORDS.get(cd_sign, '')
                 cd_house = self.planet_houses.get(cd_lord, 0)
 
                 if cd_house in target_houses:
                     signals.append(f'当前Chara Dasha {cd_sign}({cd_lord})在{cd_house}宫(目标宫)')
 
                 # Chara Antardasha
-                antardashas = current_cd.get('antardashas', [])
+                antardashas = current_cd.get('antardashas') or current_cd.get('antardasha', [])
                 if antardashas:
                     current_ad = antardashas[0] if antardashas else {}
                     ad_sign = current_ad.get('sign', '')
-                    ad_lord_name = SIGN_LORDS.get(ad_sign, '')
+                    ad_lord_name = current_ad.get('lord', '') or SIGN_LORDS.get(ad_sign, '')
                     ad_h = self.planet_houses.get(ad_lord_name, 0)
                     if ad_h in target_houses:
                         signals.append(f'Chara AD {ad_sign}({ad_lord_name})在{ad_h}宫(目标宫)')

scripts/jyotish_engine.py

@@ -43,7 +43,7 @@ import csv
 import math
 import sqlite3
 from datetime import datetime, timedelta
-from typing import Dict
+from typing import Dict, List
 
 # ============================================================================
 # 路径常量
@@ -219,7 +219,7 @@ def cmd_dasha(args):
     for i in range(9):
         lord = DASHA_ORDER[(si + i) % 9]; years = DASHA_YEARS[lord]
         end_dt = dt + timedelta(days=years * 365.25)
-        timeline.append({"lord": lord, "lord_cn": PLANET_CN[lord], "start": dt.strftime("%Y-%m-%d"), "end": end_dt.strftime("%Y-%m-%d"), "years": years})
+        timeline.append({"lord": lord, "lord_cn": PLANET_CN[lord], "start": dt.strftime("%Y-%m-%d"), "end": end_dt.strftime("%Y-%m-%d"), "years": years, "is_current": False})
         dt = end_dt
 
     today = datetime.strptime(args.today, "%Y-%m-%d") if args.today else datetime.now()
@@ -227,6 +227,7 @@ def cmd_dasha(args):
     for d in timeline:
         ds = datetime.strptime(d["start"], "%Y-%m-%d"); de = datetime.strptime(d["end"], "%Y-%m-%d")
         if ds <= today < de:
+            d["is_current"] = True
             total_days = (de - ds).days; li = DASHA_ORDER.index(d["lord"])
             sub = []; sdt = ds
             for j in range(9):
@@ -313,16 +314,21 @@ def cmd_predict(args):
     try:
         sys.path.insert(0, SCRIPT_DIR)
         from event_prediction_model import EventPredictionModel
-        asc_sign = chart.get("ascendant", {}).get("sign", "Unknown")
+        # 直接传完整chart数据给EventPredictionModel（v5.0需要ascendant dict和planets dict）
-        planets = chart.get("planets", {})
+        # 同时从 full-reading 输出中提取所有模块数据传入（v5.1修复：之前丢失dasha/congregation等）
-        # 构建模型需要的行星简化数据
+        modules = chart.get("modules", {})
-        planet_positions = {}
+        model = EventPredictionModel(
-        for pn, pd in planets.items():
+            chart_data={
-            if isinstance(pd, dict) and 'house' in pd:
+                "ascendant": chart.get("ascendant", {}),
-                planet_positions[pn] = {'sign': pd.get('sign', ''), 'house': pd.get('house', 0)}
+                "planets": chart.get("planets", {}),
-        model = EventPredictionModel(chart_data={"ascendant": asc_sign, "planets": planet_positions})
+            },
+            dasha_data=modules.get("dasha"),
+            congregation_data=modules.get("congregation"),
+            vivah_saham_data=modules.get("vivah_saham"),
+            chara_dasha_data=modules.get("jaimini", {}).get("chara_dasha"),
+        )
         raw_preds = model.predict_all_events()
-        # 将 Prediction dataclass 转为可序列化 dict
+        # 将 Prediction dataclass 转为可序列化 dict（v5.1补充缺失字段）
         predictions = []
         for p in raw_preds:
             predictions.append({
@@ -330,9 +336,13 @@ def cmd_predict(args):
                 "description": p.description,
                 "probability": p.probability,
                 "risk_level": str(p.risk_level.value) if hasattr(p.risk_level, 'value') else str(p.risk_level),
+                "confidence": str(p.confidence.value) if hasattr(p.confidence, 'value') else str(p.confidence),
                 "timing": p.timing,
                 "key_factors": p.key_factors,
                 "recommendations": p.recommendations,
+                "dasha_signals": p.dasha_signals,
+                "transit_signals": p.transit_signals,
+                "timing_windows": p.timing_windows,
             })
         return {
             "method": "三层验证法（EventPredictionModel规则引擎）",