每日总结更新 20260314

check_file_structure.py

@@ -0,0 +1,15 @@
+import pandas as pd
+
+# 文件路径
+file1 = "/root/.openclaw/media/inbound/é_¾åº_æ_æ_å_è_ç³_æ_1.0---8b762144-a4a3-481d-bdb8-b3b0dcbf875a.xlsx"
+file2 = "/root/.openclaw/media/inbound/â_¼ï_LV1-å_ç_å_è_åº_-ç¼_å_é_è_ç_è_é---286e16db-d460-460d-95a4-242f28a0429c.xlsx"
+
+print("===== 第一份表格结构 =====")
+df1 = pd.read_excel(file1)
+print(f"列名：{list(df1.columns)}")
+print(f"前5行数据：\n{df1.head()}\n")
+
+print("===== 第二份表格结构 =====")
+df2 = pd.read_excel(file2)
+print(f"列名：{list(df2.columns)}")
+print(f"前5行数据：\n{df2.head()}")

check_new_lib.py

@@ -0,0 +1,8 @@
+import pandas as pd
+
+final_lib_file = "/root/.openclaw/media/inbound/â_¼ï_LV1-å_ç_å_è_åº_-ç¼_å_é_è_ç_è_é---1de9de11-1a6b-45c7-856a-4d69f9b26aa9.xlsx"
+df_final = pd.read_excel(final_lib_file)
+
+print("新定稿单词库列名：", list(df_final.columns))
+print("\n前10行预览：")
+print(df_final.head(10))

check_new_word_lib.py

@@ -0,0 +1,11 @@
+import pandas as pd
+
+# 新的定稿单词库路径
+new_file = "/root/.openclaw/media/inbound/â_¼ï_LV1-å_ç_å_è_åº_-ç¼_å_é_è_ç_è_é---23d539f8-33d6-4679-b9ae-91520114ae54.xlsx"
+# 原始带详细字段的单词表路径
+origin_file = "/root/.openclaw/media/inbound/é_¾åº_æ_æ_å_è_ç³_æ_1.0---8b762144-a4a3-481d-bdb8-b3b0dcbf875a.xlsx"
+
+print("===== 新定稿单词库结构 =====")
+df_new = pd.read_excel(new_file)
+print(f"列名：{list(df_new.columns)}")
+print(f"前10行数据预览：\n{df_new.head(10)}")

check_sheets.py

@@ -0,0 +1,14 @@
+import pandas as pd
+from openpyxl import load_workbook
+
+# 最新的定稿库文件路径
+final_lib_file = "/root/.openclaw/media/inbound/â_¼ï_LV1-å_ç_å_è_åº_-ç¼_å_é_è_ç_è_é---1de9de11-1a6b-45c7-856a-4d69f9b26aa9.xlsx"
+
+# 查看所有sheet
+wb = load_workbook(final_lib_file, read_only=True)
+print(f"文件包含的sheet：{wb.sheetnames}")
+
+for sheet_name in wb.sheetnames:
+    df = pd.read_excel(final_lib_file, sheet_name=sheet_name)
+    print(f"\nsheet名称：{sheet_name}，行数：{len(df)}")
+    print(f"前3行预览：\n{df.head(3)}")

check_unit_info.py

@@ -0,0 +1,10 @@
+import pandas as pd
+
+file2 = "/root/.openclaw/media/inbound/â_¼ï_LV1-å_ç_å_è_åº_-ç¼_å_é_è_ç_è_é---286e16db-d460-460d-95a4-242f28a0429c.xlsx"
+df2 = pd.read_excel(file2)
+
+print(f"第二份表格总单词数：{len(df2)}")
+print("\n所有占用情况唯一值：")
+units = df2['占用情况'].dropna().unique()
+for unit in units:
+    print(unit)

check_word_match.py

@@ -0,0 +1,41 @@
+import pandas as pd
+
+# 文件路径
+final_lib_file = "/root/.openclaw/media/inbound/â_¼ï_LV1-å_ç_å_è_åº_-ç¼_å_é_è_ç_è_é---1de9de11-1a6b-45c7-856a-4d69f9b26aa9.xlsx" # 定稿单词库
+difficulty_file = "/root/.openclaw/media/inbound/é_¾åº_æ_æ_å_è_ç³_æ_1.0---a5011ea1-5bef-47af-be44-633db83f822e.xlsx" # 难度表
+
+# 读取
+df_final = pd.read_excel(final_lib_file)
+df_diff = pd.read_excel(difficulty_file)
+
+# 处理定稿库单词：去空、去非字符串（比如数字）、转小写统一对比
+final_words = []
+for w in df_final['单词'].tolist():
+    if pd.notna(w) and isinstance(w, str):
+        final_words.append(w.lower())
+final_set = set(final_words)
+print(f"定稿库有效单词（纯字符串，去空）：{len(final_set)}个")
+print(f"定稿库原始总条目数：{len(df_final)}")
+print(f"定稿库非字符串/空值条目数：{len(df_final) - len(final_words)}")
+
+# 处理难度表单词
+diff_words = []
+for w in df_diff['单词'].tolist():
+    if pd.notna(w) and isinstance(w, str):
+        diff_words.append(w.lower())
+diff_set = set(diff_words)
+print(f"\n难度表有效单词：{len(diff_set)}个")
+print(f"难度表原始总条目数：{len(df_diff)}")
+
+# 差异统计
+match_count = len(diff_set & final_set)
+unmatch_count = len(diff_set - final_set)
+print(f"\n匹配上的单词数量：{match_count}")
+print(f"未匹配的单词数量：{unmatch_count}")
+
+# 查看定稿库中不是单词的内容
+print("\n定稿库中不是有效单词的内容示例：")
+for w in df_final['单词'].tolist():
+    if pd.isna(w) or not isinstance(w, str):
+        print(w, type(w))
+        break

confirm_category_rule.py

@@ -0,0 +1,33 @@
+import pandas as pd
+
+new_file = "/root/.openclaw/media/inbound/â_¼ï_LV1-å_ç_å_è_åº_-ç¼_å_é_è_ç_è_é---23d539f8-33d6-4679-b9ae-91520114ae54.xlsx"
+df_new = pd.read_excel(new_file)
+
+print(f"定稿库总单词数：{len(df_new)}")
+print("\n单元分布：")
+units = df_new['占用情况'].dropna().unique()
+units_sorted = sorted(units, key=lambda x: (int(x.split('-')[1][1:]) if x.startswith('S') else 999, int(x.split('-')[2][1:]) if len(x.split('-'))>2 else 999))
+for unit in units_sorted:
+    count = len(df_new[df_new['占用情况'] == unit])
+    print(f"{unit}: {count}个")
+
+# 统计上册（S0 + S1 U1-U6）和下册（S1 U7+）的数量
+upper_count = 0
+lower_count = 0
+for idx, row in df_new.iterrows():
+    unit = row['占用情况']
+    if pd.isna(unit) or unit == '不常见':
+        continue
+    unit = unit.strip()
+    if unit.startswith('S0-'):
+        upper_count +=1
+    elif unit.startswith('S1-U'):
+        unit_num = int(unit.split('-')[1][1:])
+        if unit_num <=6:
+            upper_count +=1
+        else:
+            lower_count +=1
+
+print(f"\n按单元统计：")
+print(f"上册单词总数（S0 + S1 U1-U6）：{upper_count}")
+print(f"下册单词总数（S1 U7+）：{lower_count}")

daily_summary.log

@@ -56,3 +56,17 @@ cat: /root/.openclaw/workspace-xiaoban/.feishu_token: No such file or directory
                                  Dload  Upload   Total   Spent    Left  Speed
 
  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0
100   261  100    18  100   243    375   5070 --:--:-- --:--:-- --:--:--  5553
 404 page not found/root/.openclaw/workspace-xiaoban/daily_summary.sh: line 42: /home/ubuntu/.nvm/versions/node/v24.14.0/bin/openclaw: No such file or directory
+[master 0fea555] 每日总结更新 20260313
+ 7 files changed, 3946 insertions(+), 1 deletion(-)
+ create mode 100644 "\345\215\225\350\257\215\351\242\204\345\244\204\347\220\206\345\205\250\350\241\250_V2\344\274\230\345\214\226\347\211\210.csv"
+ create mode 100644 "\345\215\225\350\257\215\351\242\204\345\244\204\347\220\206\345\205\250\350\241\250_\345\220\253\346\210\220\346\234\254\350\257\204\345\210\206.csv"
+ create mode 100644 "\345\215\225\350\257\215\351\242\204\345\244\204\347\220\206\345\205\250\350\241\250_\345\220\253\346\210\220\346\234\254\350\257\204\345\210\206_\346\234\200\346\226\260\347\211\210.csv"
+ create mode 100644 "\345\215\225\350\257\215\351\242\204\345\244\204\347\220\206\345\205\250\350\241\250_\346\234\200\347\273\210\344\274\230\345\214\226\347\211\210.csv"
+ create mode 100644 "\345\255\246\344\271\240\345\206\205\345\256\271\347\263\273\346\225\260\350\241\250_\350\256\241\347\256\227\345\256\214\346\210\220.xlsx"
+error: src refspec main does not match any
+error: failed to push some refs to 'https://git.valavala.com/ai_member_only/ai_member_xiaoban'
+cat: /root/.openclaw/workspace-xiaoban/.feishu_token: No such file or directory
+  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
+                                 Dload  Upload   Total   Spent    Left  Speed
+
  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0
100   261  100    18  100   243    428   5780 --:--:-- --:--:-- --:--:--  6365
+404 page not found/root/.openclaw/workspace-xiaoban/daily_summary.sh: line 42: /home/ubuntu/.nvm/versions/node/v24.14.0/bin/openclaw: No such file or directory

final_reclassify.py

@@ -0,0 +1,41 @@
+import pandas as pd
+
+# 文件路径
+final_lib_file = "/root/.openclaw/media/inbound/â_¼ï_LV1-å_ç_å_è_åº_-ç¼_å_é_è_ç_è_é---1de9de11-1a6b-45c7-856a-4d69f9b26aa9.xlsx" # 定稿单词库（两个sheet：上/下）
+difficulty_file = "/root/.openclaw/media/inbound/é_¾åº_æ_æ_å_è_ç³_æ_1.0---a5011ea1-5bef-47af-be44-633db83f822e.xlsx" # 难度表
+output_file = "/root/.openclaw/workspace-xiaoban/最终版单词上下册分类结果.xlsx"
+
+# 读取定稿库的两个sheet
+df_upper_lib = pd.read_excel(final_lib_file, sheet_name='单词表-LV1（上）')
+df_lower_lib = pd.read_excel(final_lib_file, sheet_name='单词表-LV1（下）')
+
+# 提取上下册单词列表，去空值
+upper_words = set(df_upper_lib['单词'].dropna().tolist())
+lower_words = set(df_lower_lib['单词'].dropna().tolist())
+
+print(f"定稿库上册单词数：{len(upper_words)}")
+print(f"定稿库下册单词数：{len(lower_words)}")
+print(f"合计：{len(upper_words)+len(lower_words)}")
+
+# 读取难度表
+df_diff = pd.read_excel(difficulty_file)
+
+# 匹配分类
+df_diff['分类'] = df_diff['单词'].apply(lambda x: '上册' if x in upper_words else '下册' if x in lower_words else '未匹配')
+
+# 拆分结果
+df_upper = df_diff[df_diff['分类'] == '上册'].drop(columns=['分类'])
+df_lower = df_diff[df_diff['分类'] == '下册'].drop(columns=['分类'])
+df_other = df_diff[df_diff['分类'] == '未匹配'].drop(columns=['分类'])
+
+# 写入结果
+with pd.ExcelWriter(output_file, engine='openpyxl') as writer:
+    df_upper.to_excel(writer, sheet_name='上册单词（最终版）', index=False)
+    df_lower.to_excel(writer, sheet_name='下册单词（最终版）', index=False)
+    if len(df_other) >0:
+        df_other.to_excel(writer, sheet_name='未匹配单词', index=False)
+
+print(f"\n处理完成！结果已保存到：{output_file}")
+print(f"上册匹配到单词数：{len(df_upper)}")
+print(f"下册匹配到单词数：{len(df_lower)}")
+print(f"未匹配到单词数：{len(df_other)}")

generate_teaching_scheme.py

@@ -0,0 +1,72 @@
+import pandas as pd
+
+# 你提供的核心逻辑，适配Excel输入输出
+def process_vocabulary_system(file_path):
+    # 1. 加载Excel数据
+    try:
+        df = pd.read_excel(file_path)
+    except FileNotFoundError:
+        return "Error: File not found."
+
+    df.columns = [c.strip() for c in df.columns]
+    print(f"加载文件成功，共{len(df)}条单词记录")
+
+    # 2. 你定义的特殊规则
+    t2_special_list = {
+        'invisible': {'air', 'wind', 'smoke', 'gas'},
+        'abstract': {'song', 'friend', 'hobby', 'art', 'pe', 'music', 'fun'},
+        'generalized': {'child', 'children', 'father', 'mother', 'food', 'colour', 'animal', 'toy'},
+        'identity': {'address', 'age', 'aunt', 'name'}
+    }
+
+    # 预展开T2特殊词集合
+    all_t2_special = {item for sublist in t2_special_list.values() for item in sublist}
+
+    # 3. 核心处理逻辑
+    def apply_rules(row):
+        # 清洗输入
+        word = str(row.get('单词', '')).lower().strip()
+        t_score = pd.to_numeric(row.get('实现成本(T)', 1), errors='coerce')
+        if pd.isna(t_score):
+            t_score = 1
+
+        # 规则分支
+        if t_score >= 3:
+            scheme = "逻辑交互 / UI 处理"
+            reason = "英语骨架词。涉及空间位置、时序或数量的逻辑判定，需系统重度UI引导。"
+            link = "建议设计‘解谜指令’，如：利用 here/there 进行远近空间坐标对比任务。"
+
+        elif t_score == 2 or word in all_t2_special:
+            scheme = "动画 / 特效 / UI处理"
+            if word in t2_special_list['invisible']:
+                reason = "隐形名词。需环境联动（如风吹树叶）和特效辅助表现。"
+                link = "联动关联实物，如：wind 联动 tree/leaf 的动态表现。"
+            elif word in t2_special_list['generalized']:
+                reason = "泛化概念。无法用单一图片代表，需UI组合展示或多模型联动。"
+                link = f"联动具体成员，由 {word} 展示其下属的 T1 级具象单词集合。"
+            elif word in t2_special_list['abstract'] or word in t2_special_list['identity']:
+                reason = "抽象/身份信息。需通过情节演绎或特定 UI 界面（如家谱）界定。"
+                link = "联动相关动作，如：song 联动 sing；age 联动 numbers。"
+            else:
+                reason = "动作/状态词。需 Animator 动画、粒子特效或角色表情反馈。"
+                link = "建议设计状态切换任务，如：open vs closed；dirty vs clean。"
+
+        else: # T1 情况
+            scheme = "静态模型展示"
+            reason = "具象实物。在 Unity 中对应单一、静态的物理模型或材质资源。"
+            link = "可作为背景或道具。建议联动颜色词或方位词增加任务厚度。"
+
+        return pd.Series([scheme, reason, link])
+
+    # 执行规则生成新列
+    df[['教学方案展示', '实现理由', '联动建议']] = df.apply(apply_rules, axis=1)
+
+    # 4. 导出为Excel
+    output_file = "/root/.openclaw/workspace-xiaoban/LV1词汇教学方案生成结果.xlsx"
+    df.to_excel(output_file, index=False)
+    return f"Success: 处理完成，结果已保存到 {output_file}"
+
+# 处理刚收到的LV1词汇表
+input_path = "/root/.openclaw/media/inbound/â_¼ï_LV1-å_ç_å_è_åº_-ç¼_å_é_è_ç_è_é---d41d887f-5d65-4eab-928d-a717e5097e8c.xlsx"
+result = process_vocabulary_system(input_path)
+print(result)

match_columns.py

@@ -0,0 +1,43 @@
+import pandas as pd
+
+# 文件路径
+table1_path = "/root/.openclaw/media/inbound/é_¾åº_æ_æ_å_è_ç³_æ_1.0---4d1d9fe3-1e36-4df1-baf6-d826fcf7a05e.xlsx"
+table3_path = "/root/.openclaw/media/inbound/â_¼ï_LV1-å_ç_å_è_åº_-ç¼_å_é_è_ç_è_é---e503b23c-829e-4367-b819-762856bd50b5.xlsx"
+output_path = "/root/.openclaw/workspace-xiaoban/匹配完成的LV1词汇表.xlsx"
+
+# 读取两个表格
+df1 = pd.read_excel(table1_path)
+df3 = pd.read_excel(table3_path)
+
+print(f"表一总条数：{len(df1)}")
+print(f"表三总条数：{len(df3)}")
+print(f"表一列名：{list(df1.columns)}")
+print(f"表三列名：{list(df3.columns)}")
+
+# 创建映射：统一将单词转为字符串作为key，匹配三个字段
+word_map = {}
+for _, row in df1.iterrows():
+    word = str(row['单词']).strip()
+    word_map[word] = {
+        '难度（D）': row['难度（D）'],
+        '实现成本(T)': row['实现成本(T)'],
+        '单词系数': row['单词系数']
+    }
+
+# 给表三添加三列
+def get_value(word, col):
+    key = str(word).strip()
+    return word_map.get(key, {}).get(col, None)
+
+df3['难度（D）'] = df3['单词'].apply(lambda x: get_value(x, '难度（D）'))
+df3['实现成本(T)'] = df3['单词'].apply(lambda x: get_value(x, '实现成本(T)'))
+df3['单词系数'] = df3['单词'].apply(lambda x: get_value(x, '单词系数'))
+
+# 保存结果
+df3.to_excel(output_path, index=False)
+
+# 统计匹配情况
+match_count = df3['难度（D）'].notna().sum()
+print(f"\n匹配完成！结果已保存到：{output_path}")
+print(f"成功匹配条数：{match_count}")
+print(f"未匹配条数：{len(df3) - match_count}")

match_lower_final.py

@@ -0,0 +1,40 @@
+import pandas as pd
+
+# 文件路径
+difficulty_path = "/root/.openclaw/media/inbound/é_¾åº_æ_æ_å_è_ç³_æ_1.0---4d1d9fe3-1e36-4df1-baf6-d826fcf7a05e.xlsx" # 难度_成本单词系数1.0表
+lower_path = "/root/.openclaw/media/inbound/â_¼ï_LV1-å_ç_å_è_åº_-ç¼_å_é_è_ç_è_é---59ff96e7-d862-476b-be16-3162afcd818f.xlsx" # 最新的下册单词表
+output_path = "/root/.openclaw/workspace-xiaoban/最终版_LV1下册词汇匹配系数结果.xlsx"
+
+# 读取表格
+df_diff = pd.read_excel(difficulty_path)
+df_lower = pd.read_excel(lower_path)
+
+print(f"下册单词表总条数：{len(df_lower)}")
+
+# 创建映射字典，所有单词统一转为字符串匹配，包含数字
+word_map = {}
+for _, row in df_diff.iterrows():
+    word_key = str(row['单词']).strip()
+    word_map[word_key] = {
+        '难度（D）': row['难度（D）'],
+        '实现成本(T)': row['实现成本(T)'],
+        '单词系数': row['单词系数']
+    }
+
+# 匹配字段
+def match_field(word, field):
+    key = str(word).strip()
+    return word_map.get(key, {}).get(field, None)
+
+df_lower['难度（D）'] = df_lower['单词'].apply(lambda x: match_field(x, '难度（D）'))
+df_lower['实现成本(T)'] = df_lower['单词'].apply(lambda x: match_field(x, '实现成本(T)'))
+df_lower['单词系数'] = df_lower['单词'].apply(lambda x: match_field(x, '单词系数'))
+
+# 保存结果
+df_lower.to_excel(output_path, index=False)
+
+# 统计
+success_count = df_lower['难度（D）'].notna().sum()
+print(f"\n匹配完成！结果已保存到：{output_path}")
+print(f"成功匹配条数：{success_count}")
+print(f"未匹配条数：{len(df_lower) - success_count}")

match_lv1_lower.py

@@ -0,0 +1,39 @@
+import pandas as pd
+
+# 文件路径
+difficulty_path = "/root/.openclaw/media/inbound/é_¾åº_æ_æ_å_è_ç³_æ_1.0---4d1d9fe3-1e36-4df1-baf6-d826fcf7a05e.xlsx" # 难度表
+lv1_lower_path = "/root/.openclaw/media/inbound/â_¼ï_LV1-å_ç_å_è_åº_-ç¼_å_é_è_ç_è_é---5b90d819-abf3-4882-8772-ed8f3e0b449f.xlsx" # LV1下册词汇表
+output_path = "/root/.openclaw/workspace-xiaoban/正确版_LV1下册词汇匹配结果.xlsx"
+
+# 读取表格
+df_diff = pd.read_excel(difficulty_path)
+df_lower = pd.read_excel(lv1_lower_path)
+
+print(f"LV1下册词汇表总条数：{len(df_lower)}")
+
+# 创建难度表映射（全部单词，不区分上下册，按内容匹配）
+word_map = {}
+for _, row in df_diff.iterrows():
+    word = str(row['单词']).strip()
+    word_map[word] = {
+        '难度（D）': row['难度（D）'],
+        '实现成本(T)': row['实现成本(T)'],
+        '单词系数': row['单词系数']
+    }
+
+# 匹配字段
+def get_value(word, col):
+    key = str(word).strip()
+    return word_map.get(key, {}).get(col, None)
+
+df_lower['难度（D）'] = df_lower['单词'].apply(lambda x: get_value(x, '难度（D）'))
+df_lower['实现成本(T)'] = df_lower['单词'].apply(lambda x: get_value(x, '实现成本(T)'))
+df_lower['单词系数'] = df_lower['单词'].apply(lambda x: get_value(x, '单词系数'))
+
+# 保存结果
+df_lower.to_excel(output_path, index=False)
+
+match_count = df_lower['难度（D）'].notna().sum()
+print(f"\nLV1下册匹配完成！结果已保存到：{output_path}")
+print(f"成功匹配条数：{match_count}")
+print(f"未匹配条数：{len(df_lower) - match_count}")

match_remaining.py

@@ -0,0 +1,41 @@
+import pandas as pd
+
+# 文件路径
+table1_path = "/root/.openclaw/media/inbound/é_¾åº_æ_æ_å_è_ç³_æ_1.0---4d1d9fe3-1e36-4df1-baf6-d826fcf7a05e.xlsx"
+table2_path = "/root/.openclaw/media/inbound/â_¼ï_LV1-å_ç_å_è_åº_-ç¼_å_é_è_ç_è_é---5b90d819-abf3-4882-8772-ed8f3e0b449f.xlsx" # 剩下的480行
+output_path = "/root/.openclaw/workspace-xiaoban/匹配完成的LV1下册词汇表.xlsx"
+
+# 读取表格
+df1 = pd.read_excel(table1_path)
+df2 = pd.read_excel(table2_path)
+
+print(f"表一总条数：{len(df1)}")
+print(f"待处理的下册表总条数：{len(df2)}")
+
+# 创建映射
+word_map = {}
+for _, row in df1.iterrows():
+    word = str(row['单词']).strip()
+    word_map[word] = {
+        '难度（D）': row['难度（D）'],
+        '实现成本(T)': row['实现成本(T)'],
+        '单词系数': row['单词系数']
+    }
+
+# 匹配字段
+def get_value(word, col):
+    key = str(word).strip()
+    return word_map.get(key, {}).get(col, None)
+
+df2['难度（D）'] = df2['单词'].apply(lambda x: get_value(x, '难度（D）'))
+df2['实现成本(T)'] = df2['单词'].apply(lambda x: get_value(x, '实现成本(T)'))
+df2['单词系数'] = df2['单词'].apply(lambda x: get_value(x, '单词系数'))
+
+# 保存
+df2.to_excel(output_path, index=False)
+
+# 统计
+match_count = df2['难度（D）'].notna().sum()
+print(f"\n处理完成！结果已保存到：{output_path}")
+print(f"成功匹配条数：{match_count}")
+print(f"未匹配条数：{len(df2) - match_count}")

new_reclassify.py

@@ -0,0 +1,42 @@
+import pandas as pd
+
+# 文件路径
+final_lib_file = "/root/.openclaw/media/inbound/â_¼ï_LV1-å_ç_å_è_åº_-ç¼_å_é_è_ç_è_é---1de9de11-1a6b-45c7-856a-4d69f9b26aa9.xlsx" # 第一份：定稿单词库（仅单词列表）
+difficulty_file = "/root/.openclaw/media/inbound/é_¾åº_æ_æ_å_è_ç³_æ_1.0---a5011ea1-5bef-47af-be44-633db83f822e.xlsx" # 第二份：难度表
+output_file = "/root/.openclaw/workspace-xiaoban/最新定稿版单词上下册分类结果.xlsx"
+
+# 读取两个表格
+df_final = pd.read_excel(final_lib_file)
+df_diff = pd.read_excel(difficulty_file)
+
+# 提取定稿单词列表，去空值，去重
+final_words = df_final['单词'].dropna().unique().tolist()
+total = len(final_words)
+print(f"定稿单词库总有效不重复单词数：{total}")
+
+# 按照定稿库顺序：前一半上册，后一半下册
+upper_words = set(final_words[:total//2])
+lower_words = set(final_words[total//2:])
+
+print(f"上册单词数：{len(upper_words)}")
+print(f"下册单词数：{len(lower_words)}")
+
+# 分类难度表单词匹配分类
+df_diff['分类'] = df_diff['单词'].apply(lambda x: '上册' if x in upper_words else '下册' if x in lower_words else '未匹配')
+
+# 拆分结果
+df_upper = df_diff[df_diff['分类'] == '上册'].drop(columns=['分类'])
+df_lower = df_diff[df_diff['分类'] == '下册'].drop(columns=['分类'])
+df_other = df_diff[df_diff['分类'] == '未匹配'].drop(columns=['分类'])
+
+# 写入结果
+with pd.ExcelWriter(output_file, engine='openpyxl') as writer:
+    df_upper.to_excel(writer, sheet_name='上册单词', index=False)
+    df_lower.to_excel(writer, sheet_name='下册单词', index=False)
+    if len(df_other) >0:
+        df_other.to_excel(writer, sheet_name='未匹配单词', index=False)
+
+print(f"\n处理完成！结果已保存到：{output_file}")
+print(f"上册匹配到单词数：{len(df_upper)}")
+print(f"下册匹配到单词数：{len(df_lower)}")
+print(f"未匹配到单词数：{len(df_other)}")

process_word_list.py

@@ -0,0 +1,53 @@
+import pandas as pd
+from openpyxl import load_workbook
+
+# 文件路径
+file1 = "/root/.openclaw/media/inbound/é_¾åº_æ_æ_å_è_ç³_æ_1.0---8b762144-a4a3-481d-bdb8-b3b0dcbf875a.xlsx"
+file2 = "/root/.openclaw/media/inbound/â_¼ï_LV1-å_ç_å_è_åº_-ç¼_å_é_è_ç_è_é---286e16db-d460-460d-95a4-242f28a0429c.xlsx"
+output_file = "/root/.openclaw/workspace-xiaoban/单词上下分类结果.xlsx"
+
+# 读取第一个表格（带详细字段的单词表）
+df1 = pd.read_excel(file1)
+# 读取第二个表格（LV1词汇表）
+df2 = pd.read_excel(file2)
+
+# 给第二份表格添加上下分类
+def get_category(unit):
+    if pd.isna(unit) or unit == '不常见':
+        return '其他'
+    unit = unit.strip()
+    if unit.startswith('S0-'):
+        return '上'
+    if unit.startswith('S1-U'):
+        # 提取单元号
+        unit_num = int(unit.split('-')[1][1:])
+        if unit_num <= 6:
+            return '上'
+        else:
+            return '下'
+    return '其他'
+
+df2['分类'] = df2['占用情况'].apply(get_category)
+
+# 创建单词到分类的映射
+word_category_map = df2.drop_duplicates('单词').set_index('单词')['分类'].to_dict()
+
+# 给第一份表格添加分类列
+df1['分类'] = df1['单词'].map(word_category_map)
+
+# 拆分分类
+df_upper = df1[df1['分类'] == '上'].drop(columns=['分类'])
+df_lower = df1[df1['分类'] == '下'].drop(columns=['分类'])
+df_other = df1[df1['分类'] == '其他'].drop(columns=['分类'])
+
+# 写入结果到Excel，分三个sheet
+with pd.ExcelWriter(output_file, engine='openpyxl') as writer:
+    df_upper.to_excel(writer, sheet_name='上册单词', index=False)
+    df_lower.to_excel(writer, sheet_name='下册单词', index=False)
+    if len(df_other) > 0:
+        df_other.to_excel(writer, sheet_name='其他分类单词', index=False)
+
+print(f"处理完成！结果已保存到：{output_file}")
+print(f"上册单词数量：{len(df_upper)}")
+print(f"下册单词数量：{len(df_lower)}")
+print(f"其他分类单词数量：{len(df_other)}")

reclassify_simple.py

@@ -0,0 +1,28 @@
+import pandas as pd
+
+# 文件路径
+final_lib_file = "/root/.openclaw/media/inbound/â_¼ï_LV1-å_ç_å_è_åº_-ç¼_å_é_è_ç_è_é---1de9de11-1a6b-45c7-856a-4d69f9b26aa9.xlsx" # 定稿单词库
+difficulty_file = "/root/.openclaw/media/inbound/é_¾åº_æ_æ_å_è_ç³_æ_1.0---a5011ea1-5bef-47af-be44-633db83f822e.xlsx" # 难度表
+output_file = "/root/.openclaw/workspace-xiaoban/极简版单词上下册分类结果.xlsx"
+
+# 读取表格
+df_final = pd.read_excel(final_lib_file)
+df_diff = pd.read_excel(difficulty_file)
+
+# 完全按原始顺序拆分：前250行上册，后250行下册，无视内容
+final_words_all = df_final['单词'].tolist()
+upper_words = final_words_all[:250]
+lower_words = final_words_all[250:]
+
+# 直接匹配，无视重复
+upper_df = df_diff[df_diff['单词'].isin(upper_words)]
+lower_df = df_diff[df_diff['单词'].isin(lower_words)]
+
+# 写入结果
+with pd.ExcelWriter(output_file, engine='openpyxl') as writer:
+    upper_df.to_excel(writer, sheet_name='上册单词', index=False)
+    lower_df.to_excel(writer, sheet_name='下册单词', index=False)
+
+print(f"处理完成！结果已保存到：{output_file}")
+print(f"上册单词数量：{len(upper_df)}")
+print(f"下册单词数量：{len(lower_df)}")

reclassify_word.py

@@ -0,0 +1,52 @@
+import pandas as pd
+from openpyxl import load_workbook
+
+# 文件路径
+origin_file = "/root/.openclaw/media/inbound/é_¾åº_æ_æ_å_è_ç³_æ_1.0---8b762144-a4a3-481d-bdb8-b3b0dcbf875a.xlsx"
+final_lib_file = "/root/.openclaw/media/inbound/â_¼ï_LV1-å_ç_å_è_åº_-ç¼_å_é_è_ç_è_é---23d539f8-33d6-4679-b9ae-91520114ae54.xlsx"
+output_file = "/root/.openclaw/workspace-xiaoban/定稿版单词上下册分类结果.xlsx"
+
+# 读取原始单词表（带详细字段）
+df_origin = pd.read_excel(origin_file)
+# 读取定稿单词库
+df_final = pd.read_excel(final_lib_file)
+
+# 给定稿库单词添加上下册分类
+def get_category(unit):
+    if pd.isna(unit) or unit.strip() == '' or unit.strip() == '不常见':
+        return '不匹配'
+    unit = unit.strip()
+    if unit.startswith('S0-'):
+        return '上册'
+    if unit.startswith('S1-U'):
+        unit_num = int(unit.split('-')[1][1:])
+        if unit_num <=6:
+            return '上册'
+        else:
+            return '下册'
+    return '不匹配'
+
+df_final['分类'] = df_final['占用情况'].apply(get_category)
+
+# 创建单词到分类的映射（仅包含定稿库中存在的单词）
+word_category_map = df_final[df_final['分类'] != '不匹配'].drop_duplicates('单词').set_index('单词')['分类'].to_dict()
+
+# 给原始单词表匹配分类
+df_origin['分类'] = df_origin['单词'].map(word_category_map)
+
+# 拆分上下册
+df_upper = df_origin[df_origin['分类'] == '上册'].drop(columns=['分类'])
+df_lower = df_origin[df_origin['分类'] == '下册'].drop(columns=['分类'])
+df_other = df_origin[~df_origin['分类'].isin(['上册', '下册'])].drop(columns=['分类'])
+
+# 写入结果
+with pd.ExcelWriter(output_file, engine='openpyxl') as writer:
+    df_upper.to_excel(writer, sheet_name='上册单词（定稿版）', index=False)
+    df_lower.to_excel(writer, sheet_name='下册单词（定稿版）', index=False)
+    if len(df_other) > 0:
+        df_other.to_excel(writer, sheet_name='未匹配到定稿库的单词', index=False)
+
+print(f"处理完成！结果已保存到：{output_file}")
+print(f"上册匹配到单词数量：{len(df_upper)}")
+print(f"下册匹配到单词数量：{len(df_lower)}")
+print(f"未匹配到定稿库的单词数量：{len(df_other)}")

tmp_daily_summary.md

@@ -1,3 +1,3 @@
-=== 每日总结 20260313 ===
+=== 每日总结 20260314 ===
 ## 昨日关键进展
 无昨日记忆记录