feat(aia): Complete AIA V2.0 with universal streaming capabilities

Major Changes:
- Add StreamingService with OpenAI Compatible format
- Upgrade Chat component V2 with Ant Design X integration
- Implement AIA module with 12 intelligent agents
- Update API routes to unified /api/v1 prefix
- Update system documentation

Backend (~1300 lines):
- common/streaming: OpenAI Compatible adapter
- modules/aia: 12 agents, conversation service, streaming integration
- Update route versions (RVW, PKB to v1)

Frontend (~3500 lines):
- modules/aia: AgentHub + ChatWorkspace (100% prototype restoration)
- shared/Chat: AIStreamChat, ThinkingBlock, useAIStream Hook
- Update API endpoints to v1

Documentation:
- AIA module status guide
- Universal capabilities catalog
- System overview updates
- All module documentation sync

Tested: Stream response verified, authentication working
Status: AIA V2.0 core completed (85%)

docs/03-业务模块/DC-数据清洗整理/04-开发计划/工具C_AI_Few-shot示例库.md

@@ -1,48 +1,48 @@
-# 工具C - AI Copilot Few-shot示例库
+# 撌亙<EFBFBD>C - AI Copilot Few-shot蝷箔<EFBFBD>摨?
 
 > **<2A><>﹝<EFBFBD><EFB99D>𧋦**: V1.0  
 > **<2A>𥕦遣<F0A595A6>交<EFBFBD>**: 2025-12-06  
-> **用途**: System Prompt中的Few-shot示例  
-> **覆盖场景**: 从基础清洗到高级插补，10个核心场景
+> **<EFBFBD>券<EFBFBD>?*: System Prompt銝剔<EFBFBD>Few-shot蝷箔<EFBFBD>  
+> **閬<EFBFBD><EFBFBD><EFBFBD>箸艶**: 隞𤾸抅蝖<E68A85>皜<EFBFBD><E79A9C><EFBFBD>圈<EFBFBD>蝥扳<E89DA5>銵伐<E98AB5>10銝芣瓲敹<E793B2>㦤<EFBFBD>?
 
 ---
 
 ## <20><> 蝷箔<E89DB7><E7AE94>餉<EFBFBD>
 
-| 编号 | 场景名称 | 级别 | 技术要点 | 医疗价值 |
+| 蝻硋噡 | <20>箸艶<E7AEB8>滨妍 | 蝥批<E89DA5> | <20><><EFBFBD>航<EFBFBD><E888AA>?| <20>餌<EFBFBD>隞瑕<E99A9E>?|
 |------|---------|------|---------|---------|
-| 1 | 统一缺失值标记 | Level 1 | replace | 数据标准化 ⭐⭐⭐ |
-| 2 | 数值列清洗 | Level 1 | 正则+类型转换 | 检验值处理 ⭐⭐⭐⭐ |
-| 3 | 分类变量编码 | Level 2 | map | 统计建模 ⭐⭐⭐⭐⭐ |
+| 1 | 蝏煺<EFBFBD>蝻箏仃<EFBFBD>潭<EFBFBD>霈?| Level 1 | replace | <EFBFBD>唳旿<EFBFBD><EFBFBD><EFBFBD><EFBFBD>?潃鐥<E6BD83>潃?|
+| 2 | <EFBFBD>啣<EFBFBD>澆<EFBFBD>皜<EFBFBD><EFBFBD> | Level 1 | 甇<EFBFBD><EFBFBD>+蝐餃<E89D90>頧祆揢 | 璉<>撉<EFBFBD><E69289>澆<EFBFBD><E6BE86>?潃鐥<E6BD83>潃鐥<E6BD83> |
+| 3 | <EFBFBD><EFBFBD>掩<EFBFBD>㗛<EFBFBD>蝻𣇉<EFBFBD> | Level 2 | map | 蝏蠘恣撱箸芋 潃鐥<E6BD83>潃鐥<E6BD83>潃?|
 | 4 | 餈䂿賒<E482BF>㗛<EFBFBD><E3979B><EFBFBD>拳 | Level 2 | cut | <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> 潃鐥<E6BD83>潃鐥<E6BD83> |
-| 5 | BMI计算与分类 | Level 3 | 公式+条件 | 临床指标 ⭐⭐⭐⭐⭐ |
-| 6 | 日期计算 | Level 3 | datetime | 时间间隔 ⭐⭐⭐⭐⭐ |
-| 7 | 条件筛选 | Level 3 | 多条件过滤 | 入组标准 ⭐⭐⭐⭐⭐ |
-| 8 | 简单缺失值填补 | Level 4 | fillna | 缺失处理 ⭐⭐⭐⭐ |
-| 9 | 多重插补(MICE) | Level 4 | IterativeImputer | 高级填补 ⭐⭐⭐⭐⭐ |
+| 5 | BMI霈∠<EFBFBD>銝𤾸<EFBFBD>蝐?| Level 3 | <EFBFBD>砍<EFBFBD>+<2B>∩辣 | 銝游<E98A9D><E6B8B8><EFBFBD><EFBFBD> 潃鐥<E6BD83>潃鐥<E6BD83>潃?|
+| 6 | <EFBFBD>交<EFBFBD>霈∠<EFBFBD> | Level 3 | datetime | <EFBFBD>園𡢿<EFBFBD>湧<EFBFBD> 潃鐥<E6BD83>潃鐥<E6BD83>潃?|
+| 7 | <EFBFBD>∩辣蝑偦<EFBFBD>?| Level 3 | 憭𡁏辺隞嗉<EFBFBD>皛?| <20>亦<EFBFBD><E4BAA6><EFBFBD><EFBFBD> 潃鐥<E6BD83>潃鐥<E6BD83>潃?|
+| 8 | 蝞<EFBFBD><EFBFBD>閧撩憭勗<EFBFBD>澆‵銵?| Level 4 | fillna | 蝻箏仃憭<EFBFBD><EFBFBD> 潃鐥<E6BD83>潃鐥<E6BD83> |
+| 9 | 憭𡁻<EFBFBD><EFBFBD>坿‘(MICE) | Level 4 | IterativeImputer | 擃条漣憛怨‘ 潃鐥<E6BD83>潃鐥<E6BD83>潃?|
 | 10 | <20>箄<EFBFBD><E7AE84>駁<EFBFBD> | Level 4 | sort+drop_duplicates | <20>唳旿韐券<E99F90> 潃鐥<E6BD83>潃鐥<E6BD83> |
 
 ---
 
-## 🎯 Level 1: 基础数据清洗（2个）
+## <EFBFBD>㴓 Level 1: <EFBFBD>箇<EFBFBD><EFBFBD>唳旿皜<EFBFBD><EFBFBD>嚗?銝迎<E98A9D>
 
-### 示例1: 统一缺失值标记
+### 蝷箔<EFBFBD>1: 蝏煺<E89D8F>蝻箏仃<E7AE8F>潭<EFBFBD>霈?
 
 **<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘**:
 ```
-把所有代表缺失的符号（-、不详、NA、N/A）统一替换为标准空值
+<EFBFBD>𦠜<EFBFBD><EFBFBD>劐誨銵函撩憭梁<EFBFBD>蝚血噡嚗?<3F><><EFBFBD>霂艾<E99C82><E889BE>A<EFBFBD><41>/A嚗厩<E59A97>銝<EFBFBD><E98A9D>踵揢銝箸<E98A9D><E7AEB8><EFBFBD>征<EFBFBD>?
 ```
 
 **AI<41><49><EFBFBD>隞<EFBFBD><E99A9E>**:
 ```python
-# 统一缺失值标记
+# 蝏煺<EFBFBD>蝻箏仃<EFBFBD>潭<EFBFBD>霈?
 df = df.replace(['-', '銝滩祕', 'NA', 'N/A', '\\', '<EFBFBD>芣䰻'], np.nan)
 ```
 
 **隞<><E99A9E>霂湔<E99C82>**:
-- 医疗数据常见多种缺失值表示方式
+- <EFBFBD>餌<EFBFBD><EFBFBD>唳旿撣貉<EFBFBD>憭𡁶<EFBFBD>蝻箏仃<EFBFBD>潸”蝷箸䲮撘?
 - 蝏煺<E89D8F>銝箸<E98A9D><E7AEB8><EFBFBD>aN靘蹂<E99D98><E8B982>𡒊賒蝏蠘恣<E8A098><E681A3><EFBFBD>
-- 适用场景: 数据清洗第一步
+- <EFBFBD><EFBFBD>鍂<EFBFBD>箸艶: <20>唳旿皜<E697BF><E79A9C>蝚砌<E89D9A>甇?
 
 **憸<><E686B8>蝏𤘪<E89D8F>**:
 - <20><><EFBFBD>厰<EFBFBD><E58EB0><EFBFBD><EFBFBD>蝻箏仃<E7AE8F>潭<EFBFBD>霈啗◤蝏煺<E89D8F>銝摸aN
@@ -54,30 +54,30 @@ df = df.replace(['-', '不详', 'NA', 'N/A', '\\', '未查'], np.nan)
 
 **<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘**:
 ```
-把肌酐列里的非数字符号去掉，<0.1按0.05处理，转为数值类型
+<EFBFBD>𡃏<EFBFBD><EFBFBD>𣂼<EFBFBD><EFBFBD>𣬚<EFBFBD><EFBFBD>墧㺭摮㛖泵<EFBFBD>瑕縧<EFBFBD>㚁<EFBFBD><0.1<EFBFBD>?.05憭<EFBFBD><EFBFBD>嚗諹蓮銝箸㺭<EFBFBD>潛掩<EFBFBD>?
 ```
 
 **AI<41><49><EFBFBD>隞<EFBFBD><E99A9E>**:
 ```python
-# 处理数值列的符号
+# 憭<EFBFBD><EFBFBD><EFBFBD>啣<EFBFBD>澆<EFBFBD><EFBFBD><EFBFBD>泵<EFBFBD>?
 df['creatinine'] = df['creatinine'].astype(str).str.replace('>', '').str.replace('<', '')
 df.loc[df['creatinine'] == '0.1', 'creatinine'] = '0.05'
 df['creatinine'] = pd.to_numeric(df['creatinine'], errors='coerce')
 ```
 
 **隞<><E99A9E>霂湔<E99C82>**:
-- 检验科导出数据常含符号（>、<、+等）
+- 璉<EFBFBD>撉𣬚<EFBFBD>撖澆枂<EFBFBD>唳旿撣詨鉄蝚血噡嚗?<3F>?<3F>?蝑㚁<E89D91>
 - <20><>皜<EFBFBD><E79A9C><EFBFBD>擧<EFBFBD><E693A7>質<EFBFBD>銵峕㺭<E5B395>潸恣蝞堒<E89D9E>蝏蠘恣<E8A098><E681A3><EFBFBD>
 - errors='coerce'撠<><E692A0>瘜閗蓮<E99697>Ｙ<EFBFBD><EFBCB9>潸挽銝摸aN
 
 **憸<><E686B8>蝏𤘪<E89D8F>**:
-- 肌酐列变为纯数值类型
-- 符号被正确处理
+- <EFBFBD>屸<EFBFBD><EFBFBD>堒<EFBFBD>銝箇滲<EFBFBD>啣<EFBFBD>潛掩<EFBFBD>?
+- 蝚血噡鋡急迤蝖桀<EFBFBD><EFBFBD>?
 - 撘<>虜<EFBFBD>潸蓮銝摸aN
 
 **<EFBFBD>餌<EFBFBD><EFBFBD>箸艶**:
 - 璉<>撉峕𥁒<E5B395>𠺪<EFBFBD><F0A0BAAA>屸<EFBFBD><E5B1B8><EFBFBD>蒾蝏<E892BE><E89D8F><EFBFBD><EFBFBD><EFBFBD>蝥Ｚ<E89DA5><EFBCBA>賜<EFBFBD>
-- 常见符号：>100、<0.1、12.5+
+- 撣貉<EFBFBD>蝚血噡嚗?100<EFBFBD>?0.1<EFBFBD>?2.5+
 
 ---
 
@@ -87,28 +87,28 @@ df['creatinine'] = pd.to_numeric(df['creatinine'], errors='coerce')
 
 **<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘**:
 ```
-把性别列转为数字，男=1，女=0
+<EFBFBD>𦠜<EFBFBD>批<EFBFBD><EFBFBD>𡑒蓮銝箸㺭摮梹<EFBFBD><EFBFBD>?1嚗<31>戊=0
 ```
 
 **AI<41><49><EFBFBD>隞<EFBFBD><E99A9E>**:
 ```python
 # <20>批<EFBFBD>蝻𣇉<E89DBB>
-df['gender_code'] = df['gender'].map({'男': 1, '女': 0})
+df['gender_code'] = df['gender'].map({'<EFBFBD>?: 1, '憟?: 0})
 ```
 
 **隞<><E99A9E>霂湔<E99C82>**:
-- 将文本分类变量转为数值
+- 撠<EFBFBD><EFBFBD><EFBFBD>砍<EFBFBD>蝐餃<EFBFBD><EFBFBD>讛蓮銝箸㺭<EFBFBD>?
 - 靘蹂<E99D98>Logistic<69>𧼮<EFBFBD><F0A7BCAE><EFBFBD><EFBFBD>蝏讐<E89D8F>蝏𦦵<E89D8F>撱箸芋
-- map方法简洁高效
+- map<EFBFBD>寞<EFBFBD>蝞<EFBFBD>瘣<EFBFBD><EFBFBD><EFBFBD>?
 
 **憸<><E686B8>蝏𤘪<E89D8F>**:
-- 新增gender_code列
+- <EFBFBD>啣<EFBFBD>gender_code<EFBFBD>?
 - <20>猾<EFBFBD>1, 憟喇<E6869F>0
-- 原gender列保持不变
+- <EFBFBD>鰛ender<EFBFBD>𦯀<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>?
 
 **<EFBFBD>拙<EFBFBD><EFBFBD>箸艶**:
-- 婚姻状态: 已婚=1, 未婚=0
-- 吸烟史: 是=1, 否=0
+- 憍𡁜宏<EFBFBD>嗆<EFBFBD>? 撌脣<E6928C>=1, <EFBFBD>芸<EFBFBD>=0
+- <EFBFBD>貊<EFBFBD><EFBFBD>? <20>?1, <EFBFBD>?0
 - <20>躰<EFBFBD>蝔见漲: 撠誩郎=1, <20>苷葉=2, 擃䀝葉=3, 憭批郎=4
 
 ---
@@ -117,7 +117,7 @@ df['gender_code'] = df['gender'].map({'男': 1, '女': 0})
 
 **<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘**:
 ```
-把年龄按18岁、60岁分为未成年、成年、老年三组
+<EFBFBD>𠰴僑樴<EFBFBD><EFBFBD>18撗<EFBFBD><EFBFBD>?0撗<30><E69297>銝箸𧊋<E7AEB8>𣂼僑<F0A382BC><E58391><EFBFBD>撟氬<E6929F><E6B0AC><EFBFBD><EFBFBD>僑銝厩<E98A9D>
 ```
 
 **AI<41><49><EFBFBD>隞<EFBFBD><E99A9E>**:
@@ -125,35 +125,35 @@ df['gender_code'] = df['gender'].map({'男': 1, '女': 0})
 # 撟湧<E6929F><E6B9A7><EFBFBD><EFBFBD>
 df['age_group'] = pd.cut(df['age'], 
                          bins=[0, 18, 60, 120],
-                         labels=['未成年', '成年', '老年'],
+                         labels=['<EFBFBD>芣<EFBFBD>撟?, '<EFBFBD>𣂼僑', '<EFBFBD><EFBFBD>僑'],
                          right=False)
 ```
 
 **隞<><E99A9E>霂湔<E99C82>**:
-- 将连续变量离散化为有序分类变量
+- 撠<EFBFBD><EFBFBD>蝏剖<EFBFBD><EFBFBD>讐氖<EFBFBD><EFBFBD><EFBFBD>銝箸<EFBFBD>摨誩<EFBFBD>蝐餃<EFBFBD><EFBFBD>?
 - bins摰帋<E691B0><E5B88B>粹𡢿颲寧<E9A2B2>嚗𨧣abels摰帋<E691B0><E5B88B><EFBFBD>倌
 - right=False銵函內撌阡𡡒<E998A1>喳<EFBFBD><E596B3>粹𡢿[0,18)
 
 **憸<><E686B8>蝏𤘪<E89D8F>**:
-- 新增age_group列
-- 0-17岁→未成年
+- <EFBFBD>啣<EFBFBD>age_group<EFBFBD>?
+- 0-17撗<EFBFBD><EFBFBD><EFBFBD>芣<EFBFBD>撟?
 - 18-59撗<39><E69297><EFBFBD>𣂼僑
 - 60+撗<><E69297><EFBFBD><EFBFBD>僑
 
 **<EFBFBD>餌<EFBFBD>摨𠉛鍂**:
-- 分层分析（各年龄段发病率）
-- 卡方检验（年龄组vs疾病）
+- <EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>嚗<EFBFBD><EFBFBD>撟湧<EFBFBD>畾萄<EFBFBD><EFBFBD><EFBFBD><EFBFBD>嚗?
+- <EFBFBD>⊥䲮璉<EFBFBD>撉䕘<EFBFBD>撟湧<EFBFBD>蝏<EFBFBD>s<EFBFBD>曄<EFBFBD>嚗?
 - <20>讛膩<E8AE9B>抒<EFBFBD>霈∴<E99C88><E288B4>箇瑪<E7AE87>孵<EFBFBD>銵剁<E98AB5>
 
 ---
 
 ## <20>蘂 Level 3: <20>餃郎霈∠<E99C88>銝𡒊<E98A9D><F0A1928A>㚁<EFBFBD>3銝迎<E98A9D>
 
-### 示例5: BMI计算与分类
+### 蝷箔<EFBFBD>5: BMI霈∠<EFBFBD>銝𤾸<EFBFBD>蝐?
 
 **<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘**:
 ```
-根据身高(cm)和体重(kg)计算BMI，并标记BMI≥28为肥胖
+<EFBFBD>寞旿頨恍<EFBFBD>(cm)<29>䔶<EFBFBD><E494B6>?kg)霈∠<E99C88>BMI嚗<49>僎<EFBFBD><E5838E>扇BMI<4D>?8銝箄<E98A9D><E7AE84>?
 ```
 
 **AI<41><49><EFBFBD>隞<EFBFBD><E99A9E>**:
@@ -167,23 +167,23 @@ df['obesity'] = df['BMI'].apply(lambda x: '肥胖' if x >= 28 else '正常')
 
 **隞<><E99A9E>霂湔<E99C82>**:
 - BMI<4D>砍<EFBFBD>: 雿㯄<E99BBF>(kg) / 頨恍<E9A0A8>(m)簡
-- 中国标准: BMI≥28为肥胀
+- 銝剖𤙴<EFBFBD><EFBFBD><EFBFBD>: BMI<4D>?8銝箄<E98A9D><E7AE84><EFBFBD>
 - <20>煾<EFBFBD><E785BE>𤥁恣蝞梹<E89D9E><E6A2B9>𣳇<EFBFBD>敺芰㴓
 
 **憸<><E686B8>蝏𤘪<E89D8F>**:
 - <20>啣<EFBFBD>BMI<4D>梹<EFBFBD><E6A2B9>啣<EFBFBD>潘<EFBFBD>
-- 新增obesity列（分类）
+- <EFBFBD>啣<EFBFBD>obesity<EFBFBD>梹<EFBFBD><EFBFBD><EFBFBD>掩嚗?
 
 **銝游<E98A9D><E6B8B8><EFBFBD><EFBFBD>**:
 - <20>讐𠣕: BMI < 18.5
-- 正常: 18.5 ≤ BMI < 24
-- 超重: 24 ≤ BMI < 28
-- 肥胖: BMI ≥ 28
+- 甇<EFBFBD>虜: 18.5 <EFBFBD>?BMI < 24
+- 頞<EFBFBD><EFBFBD>: 24 <EFBFBD>?BMI < 28
+- <EFBFBD>亥<EFBFBD>: BMI <EFBFBD>?28
 
 **<EFBFBD>拙<EFBFBD><EFBFBD>箸艶**:
 - 雿栞”<E6A09E>Ｙ妖(BSA): <20>𣇉<EFBFBD><F0A38789><EFBFBD><EFBFBD>霈∠<E99C88>
-- eGFR: 肾功能评估
-- APACHE评分: 危重症评估
+- eGFR: <EFBFBD>曉<EFBFBD><EFBFBD>質<EFBFBD>隡?
+- APACHE霂<EFBFBD><EFBFBD>: <20>梢<EFBFBD><E6A2A2><EFBFBD><EFBFBD>隡?
 
 ---
 
@@ -191,7 +191,7 @@ df['obesity'] = df['BMI'].apply(lambda x: '肥胖' if x >= 28 else '正常')
 
 **<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘**:
 ```
-根据入院日期和出院日期计算住院天数
+<EFBFBD>寞旿<EFBFBD>仿堺<EFBFBD>交<EFBFBD><EFBFBD><EFBFBD>枂<EFBFBD>Ｘ𠯫<EFBFBD>蠘恣蝞𦯀<EFBFBD><EFBFBD>Ｗ予<EFBFBD>?
 ```
 
 **AI<41><49><EFBFBD>隞<EFBFBD><E99A9E>**:
@@ -208,13 +208,13 @@ df['length_of_stay'] = (df['discharge_date'] - df['admission_date']).dt.days
 - .dt.days<79>𣂼<EFBFBD>憭拇㺭
 
 **憸<><E686B8>蝏𤘪<E89D8F>**:
-- 新增length_of_stay列
+- <EFBFBD>啣<EFBFBD>length_of_stay<EFBFBD>?
 - <20>啣<EFBFBD>潛掩<E6BD9B>页<EFBFBD><E9A1B5>蓥<EFBFBD>銝箏予
 
 **<EFBFBD>餌<EFBFBD>摨𠉛鍂**:
 - 雿誯堺憭拇㺭<E68B87><E3BAAD><EFBFBD>
-- 平均住院日(ALOS)统计
-- 住院费用相关性分析
+- 撟喳<EFBFBD>雿誯堺<EFBFBD>?ALOS)蝏蠘恣
+- 雿誯堺韐寧鍂<EFBFBD>詨<EFBFBD><EFBFBD>批<EFBFBD><EFBFBD>?
 
 **<EFBFBD>拙<EFBFBD><EFBFBD>箸艶**:
 - 霈∠<E99C88>撟湧<E6929F>: (隞𠰴予 - <20>箇<EFBFBD><E7AE87>交<EFBFBD>) / 365.25
@@ -223,34 +223,34 @@ df['length_of_stay'] = (df['discharge_date'] - df['admission_date']).dt.days
 
 ---
 
-### 示例7: 条件筛选（入组标准）
+### 蝷箔<EFBFBD>7: <20>∩辣蝑偦<E89D91>㚁<EFBFBD><E39A81>亦<EFBFBD><E4BAA6><EFBFBD><EFBFBD>嚗?
 
 **<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘**:
 ```
-筛选出年龄≥18岁、诊断为糖尿病、且血糖≥7.0的患者
+蝑偦<EFBFBD>匧枂撟湧<EFBFBD><EFBFBD>?8撗<38><E69297><EFBFBD><EFBFBD><EFBFBD>凋蛹蝟硋倏<E7A18B><E5808F><EFBFBD><EFBFBD><EFBFBD>銵<EFBFBD>蝟砽竉7.0<EFBFBD><EFBFBD><EFBFBD><EFBFBD>?
 ```
 
 **AI<41><49><EFBFBD>隞<EFBFBD><E99A9E>**:
 ```python
-# 多条件筛选
+# 憭𡁏辺隞嗥<EFBFBD><EFBFBD>?
 df_selected = df[
     (df['age'] >= 18) & 
-    (df['diagnosis'] == '糖尿病') & 
+    (df['diagnosis'] == '蝟硋倏<EFBFBD>?) & 
     (df['glucose'] >= 7.0)
 ]
 ```
 
 **隞<><E99A9E>霂湔<E99C82>**:
-- 布尔索引，多条件用&连接
-- 每个条件需加括号
-- 返回满足所有条件的行
+- 撣<EFBFBD><EFBFBD>蝝Ｗ<EFBFBD>嚗<EFBFBD><EFBFBD><EFBFBD>∩辣<EFBFBD>?餈墧𦻖
+- 瘥譍葵<EFBFBD>∩辣<EFBFBD><EFBFBD><EFBFBD>䭾𡠺<EFBFBD>?
+- 餈𥪜<EFBFBD>皛∟雲<EFBFBD><EFBFBD><EFBFBD>㗇辺隞嗥<EFBFBD>銵?
 
 **憸<><E686B8>蝏𤘪<E89D8F>**:
 - <20>蚤ataFrame嚗<65><E59A97><EFBFBD>怎泵<E6808E><E6B3B5>辺隞嗥<E99A9E><E597A5><EFBFBD><EFBFBD>
 - <20>篼f銝滚<E98A9D>
 
 **銝游<E98A9D><E6B8B8>𠉛弦摨𠉛鍂**:
-- 纳入标准筛选
+- 蝥喳<EFBFBD><EFBFBD><EFBFBD><EFBFBD>蝑偦<EFBFBD>?
 - <20>㘾膄<E398BE><E88684><EFBFBD><EFBFBD>娪膄
 - 鈭𡁶<E988AD><F0A181B6><EFBFBD><EFBFBD>嚗<EFBFBD><E59A97><EFBFBD><EFBFBD>僑蝟硋倏<E7A18B><E5808F><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
 
@@ -260,7 +260,7 @@ df_selected = df[
 included = df[
     (df['age'] >= 18) & 
     (df['age'] <= 75) &
-    (df['diagnosis'].isin(['肺腺癌', '肺鳞癌'])) &
+    (df['diagnosis'].isin(['<EFBFBD>箄<EFBFBD><EFBFBD>?, '<EFBFBD>粹<EFBFBD><EFBFBD>?])) &
     (df['stage'].isin(['III', 'IV']))
 ]
 
@@ -274,43 +274,43 @@ excluded = df[
 
 ---
 
-## 🔬 Level 4: 高级数据治理（3个）
+## <EFBFBD>𥈡 Level 4: 擃条漣<EFBFBD>唳旿瘝餌<EFBFBD>嚗?銝迎<E98A9D>
 
-### 示例8: 简单缺失值填补
+### 蝷箔<EFBFBD>8: 蝞<><E89D9E>閧撩憭勗<E686AD>澆‵銵?
 
 **<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘**:
 ```
-用中位数填补BMI列的缺失值
+<EFBFBD>其葉雿齿㺭憛怨‘BMI<EFBFBD>㛖<EFBFBD>蝻箏仃<EFBFBD>?
 ```
 
 **AI<41><49><EFBFBD>隞<EFBFBD><E99A9E>**:
 ```python
-# 用中位数填补缺失值
+# <EFBFBD>其葉雿齿㺭憛怨‘蝻箏仃<EFBFBD>?
 bmi_median = df['BMI'].median()
 df['BMI'] = df['BMI'].fillna(bmi_median)
 ```
 
 **隞<><E99A9E>霂湔<E99C82>**:
-- median()计算中位数（排除NaN）
-- fillna()填补缺失值
+- median()霈∠<EFBFBD>銝凋<EFBFBD><EFBFBD>堆<EFBFBD><EFBFBD>㘾膄NaN嚗?
+- fillna()憛怨‘蝻箏仃<EFBFBD>?
 - 銝凋<E98A9D><E5878B>啣<EFBFBD>蝳餌黎<E9A48C>澆蔣<E6BE86>滚<EFBFBD>
 
 **憸<><E686B8>蝏𤘪<E89D8F>**:
-- BMI列无缺失值
+- BMI<EFBFBD>埈<EFBFBD>蝻箏仃<EFBFBD>?
 - 蝻箏仃雿滨蔭鋡思葉雿齿㺭<E9BDBF>蹂誨
 
 **憛怨‘<E680A8>寞<EFBFBD><E5AF9E>㗇𥋘**:
 | <20>寞<EFBFBD> | <20><>鍂<EFBFBD>箸艶 | 隡条<E99AA1> | 蝻箇<E89DBB> |
 |------|---------|------|------|
-| 均值 | 正态分布 | 简单 | 受离群值影响 |
-| 中位数 | 偏态分布 | 稳健 | 信息损失 |
-| 众数 | 分类变量 | 保留分布 | 可能不合理 |
+| <EFBFBD><EFBFBD><EFBFBD>?| 甇<><E79487><EFBFBD><EFBFBD>撣?| 蝞<><E89D9E>?| <20>㛖氖蝢文<E89DA2>澆蔣<E6BE86>?|
+| 銝凋<EFBFBD><EFBFBD>?| <20>𤩺<EFBFBD><F0A4A9BA><EFBFBD>撣?| 蝔喳<E89D94> | 靽⊥<E99DBD><E28AA5>笔仃 |
+| 隡埈㺭 | <20><>掩<EFBFBD>㗛<EFBFBD> | 靽萘<E99DBD><E89098><EFBFBD><EFBFBD> | <20>航<EFBFBD>銝滚<E98A9D><E6BB9A>?|
 | <20>滚<EFBFBD>憛怠<E6869B> | <20>園𡢿摨誩<E691A8> | 靽萘<E99DBD>頞见飵 | 隞<><E99A9E><EFBFBD>鍂<EFBFBD>嗅<EFBFBD> |
 
 **瘜冽<E7989C>鈭钅★**:
 - <20>𩤃<EFBFBD> 隞<><E99A9E><EFBFBD>鍂鈭𡒊撩憭梁<E686AD><5%
 - <20>𩤃<EFBFBD> <20><>挽蝻箏仃銝撤CAR嚗<52><E59A97><EFBFBD>券<EFBFBD><E588B8>箇撩憭梧<E686AD>
-- ⚠️ 可能低估标准差
+- <EFBFBD>𩤃<EFBFBD> <20>航<EFBFBD>雿𦒘摯<F0A69298><E691AF><EFBFBD>撌?
 
 ---
 
@@ -318,7 +318,7 @@ df['BMI'] = df['BMI'].fillna(bmi_median)
 
 **<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘**:
 ```
-使用多重插补法对BMI、年龄、肌酐列的缺失值进行填补
+雿輻鍂憭𡁻<EFBFBD><EFBFBD>坿‘瘜訫笆BMI<EFBFBD><EFBFBD>僑樴<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>𣂼<EFBFBD><EFBFBD><EFBFBD>撩憭勗<EFBFBD>潸<EFBFBD>銵<EFBFBD>‵銵?
 ```
 
 **AI<41><49><EFBFBD>隞<EFBFBD><E99A9E>**:
@@ -336,42 +336,42 @@ df[cols] = imputer.fit_transform(df[cols])
 
 **隞<><E99A9E>霂湔<E99C82>**:
 - MICE (Multivariate Imputation by Chained Equations)
-- 利用变量间相关性预测缺失值
-- max_iter=10: 最多迭代10次
-- random_state=0: 可复现结果
+- <EFBFBD>拍鍂<EFBFBD>㗛<EFBFBD><EFBFBD>渡㮾<EFBFBD>單<EFBFBD>折<EFBFBD>瘚讠撩憭勗<EFBFBD>?
+- max_iter=10: <EFBFBD><EFBFBD>憭朞翮隞?0甈?
+- random_state=0: <EFBFBD>臬<EFBFBD><EFBFBD>啁<EFBFBD><EFBFBD>?
 
 **蝞埈<E89D9E><E59F88>毺<EFBFBD>**:
 1. <20>嘥<EFBFBD>憛怨‘嚗<E28098><E59A97><EFBFBD><EFBFBD><EFBFBD>潘<EFBFBD>
-2. 循环迭代：
-   - 对每个有缺失的变量，用其他变量预测
-   - 更新填补值
-3. 收敛后停止
+2. 敺芰㴓餈凋誨嚗?
+   - 撖寞<EFBFBD>銝芣<EFBFBD>蝻箏仃<EFBFBD><EFBFBD><EFBFBD><EFBFBD>𧶏<EFBFBD><EFBFBD>典<EFBFBD>隞硋<EFBFBD><EFBFBD>誯<EFBFBD>瘚?
+   - <EFBFBD>湔鰵憛怨‘<EFBFBD>?
+3. <EFBFBD>嗆<EFBFBD><EFBFBD>𤾸<EFBFBD>甇?
 
 **<EFBFBD><EFBFBD>鍂<EFBFBD>箸艶**:
-- ✅ 缺失率5%-30%
-- ✅ 缺失机制为MAR（随机缺失）
-- ✅ 变量间存在相关性
-- ✅ 需要保持数据分布特征
+- <EFBFBD>?蝻箏仃<E7AE8F>?%-30%
+- <EFBFBD>?蝻箏仃<E7AE8F>箏<EFBFBD>銝撤AR嚗<52><E59A97><EFBFBD>箇撩憭梧<E686AD>
+- <EFBFBD>?<3F>㗛<EFBFBD><E3979B>游<EFBFBD><E6B8B8>函㮾<E587BD>單<EFBFBD>?
+- <EFBFBD>?<3F><>閬<EFBFBD><E996AC><EFBFBD><EFBFBD>㺭<EFBFBD>桀<EFBFBD>撣<EFBFBD>鸌敺?
 
 **隡睃飵**:
-- 利用变量间关系
+- <EFBFBD>拍鍂<EFBFBD>㗛<EFBFBD><EFBFBD>游<EFBFBD>蝟?
 - 靽脲<E99DBD><E884B2>唳旿<E594B3><E697BF><EFBFBD>
 - <20>誩<EFBFBD><E8AAA9>誩榆
-- 统计学上更合理
+- 蝏蠘恣摮虫<EFBFBD><EFBFBD>游<EFBFBD><EFBFBD>?
 
-**vs 简单填补**:
-| 指标 | 简单填补 | 多重插补 |
+**vs 蝞<EFBFBD><EFBFBD>訫‵銵?*:
+| <EFBFBD><EFBFBD><EFBFBD> | 蝞<><E89D9E>訫‵銵?| 憭𡁻<E686AD><F0A181BB>坿‘ |
 |------|---------|---------|
-| 复杂度 | 低 | 中等 |
-| 计算时间 | 快 | 较慢 |
+| 憭齿<EFBFBD>摨?| 雿?| 銝剔<E98A9D> |
+| 霈∠<EFBFBD><EFBFBD>園𡢿 | 敹?| 颲<><E9A2B2> |
 | <20>誩榆 | <20>航<EFBFBD>颲<EFBFBD>之 | 颲<><E9A2B2> |
-| 标准误 | 低估 | 准确 |
-| 分布保持 | 差 | 好 |
+| <EFBFBD><EFBFBD><EFBFBD>霂?| 雿𦒘摯 | <20><>＆ |
+| <EFBFBD><EFBFBD><EFBFBD>靽脲<EFBFBD> | 撌?| 憟?|
 
 **瘜冽<E7989C>鈭钅★**:
-- 仅适用于数值变量
-- 分类变量需先编码
-- 如有时间变量需先转换
+- 隞<EFBFBD><EFBFBD><EFBFBD>鍂鈭擧㺭<EFBFBD>澆<EFBFBD><EFBFBD>?
+- <EFBFBD><EFBFBD>掩<EFBFBD>㗛<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>?
+- 憒<EFBFBD><EFBFBD><EFBFBD>園𡢿<EFBFBD>㗛<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>蓮<EFBFBD>?
 
 ---
 
@@ -384,7 +384,7 @@ df[cols] = imputer.fit_transform(df[cols])
 
 **AI<41><49><EFBFBD>隞<EFBFBD><E99A9E>**:
 ```python
-# 智能去重：先按日期排序，再按ID去重保留最后一条
+# <EFBFBD>箄<EFBFBD><EFBFBD>駁<EFBFBD>嚗𡁜<EFBFBD><EFBFBD>㗇𠯫<EFBFBD><EFBFBD><EFBFBD>摨𧶏<EFBFBD><EFBFBD>齿<EFBFBD>ID<EFBFBD>駁<EFBFBD>靽萘<EFBFBD><EFBFBD><EFBFBD><EFBFBD>𦒘<EFBFBD><EFBFBD>?
 df['check_date'] = pd.to_datetime(df['check_date'])
 df = df.sort_values('check_date').drop_duplicates(subset=['patient_id'], keep='last')
 ```
@@ -395,19 +395,19 @@ df = df.sort_values('check_date').drop_duplicates(subset=['patient_id'], keep='l
 - keep='last'靽萘<E99DBD><E89098><EFBFBD><EFBFBD>𦒘<EFBFBD><F0A69298>∴<EFBFBD><E288B4>單<EFBFBD><E596AE>唳𠯫<E594B3><F0A0AFAB><EFBFBD>
 
 **憸<><E686B8>蝏𤘪<E89D8F>**:
-- 每个患者只保留一条记录
+- 瘥譍葵<EFBFBD><EFBFBD><EFBFBD><EFBFBD>蘨靽萘<EFBFBD>銝<EFBFBD><EFBFBD>∟扇敶?
 - 靽萘<E99DBD><E89098><EFBFBD>糓璉<E7B393><E79289>交𠯫<E4BAA4><F0A0AFAB><EFBFBD><EFBFBD>啁<EFBFBD><E59581><EFBFBD>辺
 
 **<EFBFBD>拙<EFBFBD><EFBFBD>箸艶**:
 
-**场景1: 保留数据最完整的记录**
+**<EFBFBD>箸艶1: 靽萘<E99DBD><E89098>唳旿<E594B3><E697BF>摰峕㟲<E5B395><E39FB2>扇敶?*
 ```python
 # 霈∠<E99C88>瘥讛<E798A5><E8AE9B><EFBFBD><EFBFBD><EFBFBD>游漲
 df['completeness'] = df.notna().sum(axis=1)
 df = df.sort_values('completeness', ascending=False).drop_duplicates(subset=['patient_id'], keep='first')
 ```
 
-**场景2: 多字段组合去重**
+**<EFBFBD>箸艶2: 憭𡁜<E686AD>畾萇<E795BE><E89087><EFBFBD>縧<EFBFBD>?*
 ```python
 # <20>㗇<EFBFBD><E39787><EFBFBD>D+撠梯<E692A0><E6A2AF>交<EFBFBD><E4BAA4>駁<EFBFBD>
 df = df.drop_duplicates(subset=['patient_id', 'visit_date'], keep='first')
@@ -415,13 +415,13 @@ df = df.drop_duplicates(subset=['patient_id', 'visit_date'], keep='first')
 
 **<EFBFBD>箸艶3: 憭齿<E686AD><E9BDBF>餉<EFBFBD><E9A489>駁<EFBFBD>**
 ```python
-# 优先级：日期最新 > 完整度最高
+# 隡睃<EFBFBD>蝥改<EFBFBD><EFBFBD>交<EFBFBD><EFBFBD><EFBFBD><EFBFBD>?> 摰峕㟲摨行<E691A8>擃?
 df = df.sort_values(['check_date', 'completeness'], ascending=[False, False]).drop_duplicates(subset=['patient_id'], keep='first')
 ```
 
 **<EFBFBD>餌<EFBFBD><EFBFBD>箸艶**:
-- 删除重复录入的病例
-- 多次就诊取首次/末次
+- <EFBFBD>𣳇膄<EFBFBD>滚<EFBFBD>敶訫<EFBFBD><EFBFBD><EFBFBD><EFBFBD>靘?
+- 憭𡁏活撠梯<EFBFBD><EFBFBD>㚚<EFBFBD>甈?<3F>急活
 - 璉<>撉𣬚<E69289><F0A3AC9A>𨅯縧<F0A885AF>㵪<EFBFBD><E3B5AA>𡝗<EFBFBD><F0A19D97>堆<EFBFBD>
 
 ---
@@ -432,44 +432,44 @@ df = df.sort_values(['check_date', 'completeness'], ascending=[False, False]).dr
 
 ```python
 system_prompt = f"""
-你是医疗科研数据清洗专家，负责生成Pandas代码来清洗整理数据。
+雿䭾糓<EFBFBD>餌<EFBFBD>蝘𤑳<EFBFBD><EFBFBD>唳旿皜<EFBFBD><EFBFBD>銝枏振嚗諹<EFBFBD>韐<EFBFBD><EFBFBD><EFBFBD>辥andas隞<EFBFBD><EFBFBD><EFBFBD>交<EFBFBD>瘣埈㟲<EFBFBD><EFBFBD>㺭<EFBFBD>柴<EFBFBD>?
 
-## 当前数据集信息
-- 文件名: {session.fileName}
+## 敶枏<EFBFBD><EFBFBD>唳旿<EFBFBD><EFBFBD>縑<EFBFBD>?
+- <EFBFBD><EFBFBD>辣<EFBFBD>? {session.fileName}
 - 銵峕㺭: {session.totalRows}
 - <20>埈㺭: {session.totalCols}
 - <20>堒<EFBFBD>: {', '.join(session.columns)}
 
 ## 摰匧<E691B0>閫<EFBFBD><E996AB>嚗<EFBFBD>撩<EFBFBD>塚<EFBFBD>
 1. <20>芾<EFBFBD><E88ABE>滢<EFBFBD>df<64>㗛<EFBFBD>
-2. 禁止导入os、sys等危险模块
-3. 禁止使用eval、exec等危险函数
+2. 蝳<EFBFBD>迫撖澆<EFBFBD>os<EFBFBD><EFBFBD>ys蝑匧暒<EFBFBD>拇芋<EFBFBD>?
+3. 蝳<EFBFBD>迫雿輻鍂eval<EFBFBD><EFBFBD>xec蝑匧暒<EFBFBD>拙遆<EFBFBD>?
 4. 敹<>◆餈𥡝<E9A488>撘<EFBFBD>虜憭<E8999C><E686AD>
 5. 餈𥪜<E9A488><F0A5AA9C>澆<EFBFBD>: {{"code": "...", "explanation": "..."}}
 
 ## Few-shot蝷箔<E89DB7>
 
-### 示例1: 统一缺失值标记
-用户: 把所有代表缺失的符号统一替换为标准空值
+### 蝷箔<EFBFBD>1: 蝏煺<E89D8F>蝻箏仃<E7AE8F>潭<EFBFBD>霈?
+<EFBFBD>冽<EFBFBD>: <20>𦠜<EFBFBD><F0A6A09C>劐誨銵函撩憭梁<E686AD>蝚血噡蝏煺<E89D8F><E785BA>踵揢銝箸<E98A9D><E7AEB8><EFBFBD>征<EFBFBD>?
 隞<EFBFBD><EFBFBD>:
 ```python
 df = df.replace(['-', '銝滩祕', 'NA', 'N/A'], np.nan)
 ```
 
 ### 蝷箔<E89DB7>2: <20>啣<EFBFBD>澆<EFBFBD>皜<EFBFBD><E79A9C>
-用户: 把肌酐列里的非数字符号去掉，转为数值类型
+<EFBFBD>冽<EFBFBD>: <20>𡃏<EFBFBD><F0A1838F>𣂼<EFBFBD><F0A382BC>𣬚<EFBFBD><F0A3AC9A>墧㺭摮㛖泵<E39B96>瑕縧<E79195>㚁<EFBFBD>頧砌蛹<E7A08C>啣<EFBFBD>潛掩<E6BD9B>?
 隞<EFBFBD><EFBFBD>:
 ```python
 df['creatinine'] = df['creatinine'].astype(str).str.replace('>', '').str.replace('<', '')
 df['creatinine'] = pd.to_numeric(df['creatinine'], errors='coerce')
 ```
 
-[... 其他8个示例 ...]
+[... <EFBFBD>嗡<EFBFBD>8銝芰內靘?...]
 
 ## <20>冽<EFBFBD>敶枏<E695B6>霂瑟<E99C82>
 {user_message}
 
-请生成代码并解释。
+霂瑞<EFBFBD><EFBFBD>𣂷誨<EFBFBD><EFBFBD>僎閫<EFBFBD><EFBFBD><EFBFBD>?
 """
 ```
 
@@ -477,55 +477,56 @@ df['creatinine'] = pd.to_numeric(df['creatinine'], errors='coerce')
 
 ## <20>㴓 韐券<E99F90><E588B8><EFBFBD><EFBFBD>
 
-每个示例必须满足：
-- ✅ 代码可直接运行
-- ✅ 有详细注释
-- ✅ 有明确的输入输出
-- ✅ 符合Python最佳实践
-- ✅ 考虑异常情况
-- ✅ 有医疗场景说明
+瘥譍葵蝷箔<EFBFBD>敹<EFBFBD>◆皛∟雲嚗?
+- <EFBFBD>?隞<><E99A9E><EFBFBD>舐凒<E88890>亥<EFBFBD>銵?
+- <EFBFBD>?<3F>㕑祕蝏<E7A595>釣<EFBFBD>?
+- <EFBFBD>?<3F>㗇<EFBFBD>蝖桃<E89D96>颲枏<E9A2B2>颲枏枂
+- <EFBFBD>?蝚血<E89D9A>Python<EFBFBD><EFBFBD>雿喳<EFBFBD>頝?
+- <EFBFBD>?<3F><><EFBFBD>撘<EFBFBD>虜<EFBFBD><E8999C><EFBFBD>
+- <EFBFBD>?<3F>匧龫<E58CA7>堒㦤<E5A092>航秩<E888AA>?
 
 ---
 
 ## <20><> 瘚贝<E7989A><E8B49D>其<EFBFBD>霈曇恣
 
-基于这10个示例，Day 3测试应包含：
+<EFBFBD>箔<EFBFBD>餈?0銝芰內靘页<E99D98>Day 3瘚贝<E7989A>摨𥪜<E691A8><F0A5AA9C>恬<EFBFBD>
 
-**基础测试（4个）**:
-1. 示例1测试（缺失值统一）
+**<EFBFBD>箇<EFBFBD>瘚贝<EFBFBD>嚗?銝迎<E98A9D>**:
+1. 蝷箔<EFBFBD>1瘚贝<EFBFBD>嚗<EFBFBD>撩憭勗<EFBFBD>潛<EFBFBD>銝<EFBFBD>嚗?
 2. 蝷箔<E89DB7>2瘚贝<E7989A>嚗<EFBFBD>㺭<EFBFBD>潭<EFBFBD>瘣梹<E798A3>
-3. 示例3测试（性别编码）
+3. 蝷箔<EFBFBD>3瘚贝<EFBFBD>嚗<EFBFBD><EFBFBD>批<EFBFBD>蝻𣇉<EFBFBD>嚗?
 4. 蝷箔<E89DB7>4瘚贝<E7989A>嚗<EFBFBD>僑樴<E58391><E6A8B4>蝏<EFBFBD><E89D8F>
 
-**中级测试（3个）**:
-5. 示例5测试（BMI计算）
+**銝剔漣瘚贝<EFBFBD>嚗?銝迎<E98A9D>**:
+5. 蝷箔<EFBFBD>5瘚贝<EFBFBD>嚗㇂MI霈∠<EFBFBD>嚗?
 6. 蝷箔<E89DB7>6瘚贝<E7989A>嚗<EFBFBD><E59A97><EFBFBD>Ｗ予<EFBCB7>堆<EFBFBD>
 7. 蝷箔<E89DB7>7瘚贝<E7989A>嚗<EFBFBD>辺隞嗥<E99A9E><E597A5>㚁<EFBFBD>
 
-**高级测试（3个）**:
-8. 示例8测试（中位数填补）
-9. 示例9测试（多重插补）⭐
+**擃条漣瘚贝<EFBFBD>嚗?銝迎<E98A9D>**:
+8. 蝷箔<EFBFBD>8瘚贝<EFBFBD>嚗<EFBFBD>葉雿齿㺭憛怨‘嚗?
+9. 蝷箔<EFBFBD>9瘚贝<EFBFBD>嚗<EFBFBD><EFBFBD><EFBFBD>齿<EFBFBD>銵伐<EFBFBD>潃?
 10. 蝷箔<E89DB7>10瘚贝<E7989A>嚗<EFBFBD>惣<EFBFBD>賢縧<E8B3A2>㵪<EFBFBD>
 
-**扩展测试（5个）**:
+**<EFBFBD>拙<EFBFBD>瘚贝<EFBFBD>嚗?銝迎<E98A9D>**:
 11. 瘛瑕<E7989B><E79195>箸艶瘚贝<E7989A>嚗<EFBFBD><E59A97>皜<EFBFBD><E79A9C><EFBFBD>滩恣蝞梹<E89D9E>
 12. <20>躰秤<E8BAB0>箸艶瘚贝<E7989A>嚗<EFBFBD><E59A97>銝滚<E98A9D><E6BB9A>剁<EFBFBD>
 13. 颲寧<E9A2B2><E5AFA7>箸艶瘚贝<E7989A>嚗<EFBFBD><E59A97><EFBFBD>函撩憭梧<E686AD>
-14. 自我修正测试（代码报错后重试）
+14. <EFBFBD>芣<EFBFBD>靽格迤瘚贝<EFBFBD>嚗<EFBFBD>誨<EFBFBD><EFBFBD>𥁒<EFBFBD>坔<EFBFBD><EFBFBD>滩<EFBFBD>嚗?
 15. 蝡臬<E89DA1>蝡舀<E89DA1>霂𤏪<E99C82>銝𠹺<E98A9D><F0A0B9BA>𡒶I憭<49><E686AD><EFBFBD>垍<EFBFBD><E59E8D>𣈯<EFBFBD>霂<EFBFBD><E99C82>
 
 ---
 
 ## <20><> 蝏湔擪霈啣<E99C88>
 
-| 日期 | 版本 | 修改内容 | 修改人 |
+| <EFBFBD>交<EFBFBD> | <20><>𧋦 | 靽格㺿<E6A0BC><E3BABF>捆 | 靽格㺿鈭?|
 |------|------|---------|--------|
-| 2025-12-06 | V1.0 | 初始创建，10个核心示例 | AI Assistant |
+| 2025-12-06 | V1.0 | <EFBFBD>嘥<EFBFBD><EFBFBD>𥕦遣嚗?0銝芣瓲敹<E793B2>內靘?| AI Assistant |
 
 ---
 
-**文档状态**: ✅ 已确认  
-**下一步**: 开始Day 3开发（AICodeService实现）
+**<EFBFBD><EFBFBD>﹝<EFBFBD>嗆<EFBFBD>?*: <20>?撌脩＆霈? 
+**銝衤<EFBFBD>甇?*: 撘<>憪𠵿ay 3撘<33><E69298>𡢅<EFBFBD>AICodeService摰䂿緵嚗?
+