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add_15_words.py
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add_15_words.py
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import pandas as pd
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file_l2 = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L2新版独有单词.xlsx'
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file_kb = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L2知识库-三级+A2.xlsx'
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df_l2 = pd.read_excel(file_l2)
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df_kb = pd.read_excel(file_kb)
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available = df_l2[df_l2['可用'] == 1].copy()
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new_rows = []
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for _, row in available.iterrows():
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new_rows.append({
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'单词': row['单词'],
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'词性': row['词性'],
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'中文释义': row['词义'],
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'KET/三级重合': '',
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'分类': '',
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'Unnamed: 5': '',
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'与L1重复': row['是否为L1单词'],
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'L1词性': row['L1词性'] if pd.notna(row['L1词性']) else '',
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'L1词义': row['L1词义'] if pd.notna(row['L1词义']) else '',
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'在L1中的行数': row['L1行数'] if pd.notna(row['L1行数']) else '',
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'删除': ''
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})
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df_new = pd.DataFrame(new_rows)
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df_kb = pd.concat([df_kb, df_new], ignore_index=True)
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df_kb = df_kb.sort_values(by='单词', key=lambda x: x.str.lower(), ignore_index=True)
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output_path = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L2知识库-三级+A2.xlsx'
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df_kb.to_excel(output_path, index=False)
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print(f"已添加 {len(new_rows)} 个单词")
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print(f"L2知识库总单词数: {len(df_kb)}")
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print(f"\n已保存至: {output_path}")
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print("\n添加的单词（按字母顺序）:")
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for i, row in df_new.sort_values(by='单词', key=lambda x: x.str.lower()).iterrows():
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print(f" {row['单词']:<15} {str(row['词性']):<10} {row['中文释义']:<25} L1:{row['与L1重复']} L1词性:{row['L1词性']} L1词义:{row['L1词义']}")
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add_16_words.py
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add_16_words.py
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import pandas as pd
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# 文件路径
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file_path = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L2知识库-三级+A2.xlsx'
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# 要添加的16个单词
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words_to_add = [
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('supper', 'n.', '晚餐'),
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('cafeteria', 'n.', '食堂'),
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('tidy up', 'v.', '收拾'),
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('all sorts of', 'phr.', '各种各样的'),
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('all the time', 'phr.', '一直'),
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('bring back', 'v.', '拿回'),
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('turn off', 'v.', '关掉'),
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('cabinet', 'n.', '橱柜'),
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('get back', 'v.', '拿回'),
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('costume', 'n.', '服装'),
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('equipment', 'n.', '装备'),
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('explorer', 'n.', '探险家'),
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('upload', 'v.', '上传'),
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('dot', 'n.', '点'),
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('successful', 'adj.', '成功的'),
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('give back', 'v.', '归还'),
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]
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# 按字母顺序排序
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words_to_add_sorted = sorted(words_to_add, key=lambda x: x[0].lower())
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# 读取文件
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df = pd.read_excel(file_path)
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print(f"现有行数: {len(df)}")
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# 创建新行DataFrame
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new_rows = pd.DataFrame(words_to_add_sorted, columns=['单词', '词性', '中文释义'])
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# 合并
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df_updated = pd.concat([df, new_rows], ignore_index=True)
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# 按字母顺序排序
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df_updated = df_updated.sort_values(by='单词', key=lambda x: x.astype(str).str.lower())
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# 保存
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df_updated.to_excel(file_path, index=False)
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print(f"\n添加完成！")
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print(f"添加了 {len(words_to_add)} 个新单词")
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print(f"更新后总行数: {len(df_updated)}")
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print("\n新添加的单词（按字母顺序）：")
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for i, (word, pos, meaning) in enumerate(words_to_add_sorted, 1):
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print(f"{i:2d}. {word:20s} {pos:8s} {meaning}")
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add_words.py
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add_words.py
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import pandas as pd
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# 读取文件
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file_path = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L2知识库-三级+A2.xlsx'
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df = pd.read_excel(file_path)
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print("文件列名:", df.columns.tolist())
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print(f"现有行数: {len(df)}")
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# 要添加的单词
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new_words = [
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{"单词": "cafe", "词性": "n.", "中文释义": "咖啡馆"},
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{"单词": "exhibition", "词性": "n.", "中文释义": "展览"},
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{"单词": "selfie", "词性": "n.", "中文释义": "自拍"},
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{"单词": "typing", "词性": "n.", "中文释义": "打字"},
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{"单词": "performance", "词性": "n.", "中文释义": "表演"}
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]
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# 把新单词转换为DataFrame
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new_df = pd.DataFrame(new_words)
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# 合并到原数据
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df_combined = pd.concat([df, new_df], ignore_index=True)
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# 按单词字母顺序排序（忽略大小写）
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df_sorted = df_combined.sort_values(by='单词', key=lambda x: x.str.lower())
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# 保存结果
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df_sorted.to_excel(file_path, index=False)
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print(f"\n添加完成！")
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print(f"添加了 {len(new_words)} 个新单词")
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print(f"更新后总行数: {len(df_sorted)}")
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print("\n新添加的单词:")
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for word in new_words:
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print(f"{word['单词']} - {word['词性']} - {word['中文释义']}")
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all_words_dump.txt
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all_words_dump.txt
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analyze_categories.py
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analyze_categories.py
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import pandas as pd
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file_path = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/情境分类.xlsx'
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df = pd.read_excel(file_path)
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print(f"行数: {len(df)}, 列数: {len(df.columns)}")
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print(f"列名: {list(df.columns)}")
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print()
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# 查看D列（第4列，索引3）
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col_d = df.columns[3]
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print(f"D列名称: {col_d}")
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print()
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# 查看D列所有唯一值
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unique_values = df[col_d].dropna().unique()
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print(f"D列唯一值数量: {len(unique_values)}")
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print()
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print("=== D列所有唯一分类 ===")
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for i, v in enumerate(sorted(unique_values, key=str), 1):
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print(f" {i}. {v}")
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analyze_categories_detail.py
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analyze_categories_detail.py
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import pandas as pd
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file_path = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/情境分类.xlsx'
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df = pd.read_excel(file_path)
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col_d = df.columns[3]
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col_word = df.columns[0]
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# 按分类分组，显示每个分类的单词样例
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categories = df.groupby(col_d)
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for cat, group in categories:
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words = group[col_word].dropna().tolist()
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print(f"\n{'='*60}")
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print(f"【{cat}】({len(words)}个单词)")
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print(f"样例: {', '.join(str(w) for w in words[:15])}")
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if len(words) > 15:
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print(f" ... 共{len(words)}个")
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business_knowledge/L2单词表/30-34.xlsx
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business_knowledge/L2单词表/30-34.xlsx
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business_knowledge/L2单词表/KET词汇表_完整版.xlsx
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business_knowledge/L2单词表/KET词汇表_完整版.xlsx
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business_knowledge/L2单词表/L1完整.xlsx
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business_knowledge/L2单词表/L1完整.xlsx
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business_knowledge/L2单词表/L2新版_L1重复标记.xlsx
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business_knowledge/L2单词表/L2新版_L1重复标记.xlsx
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business_knowledge/L2单词表/L2新版独有单词.xlsx
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business_knowledge/L2单词表/L2新版独有单词.xlsx
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business_knowledge/L2单词表/L2知识库-三级+A2.xlsx
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business_knowledge/L2单词表/L2知识库-三级+A2.xlsx
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business_knowledge/L2单词表/三级单词表原始版.xlsx
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business_knowledge/L2单词表/三级单词表原始版.xlsx
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business_knowledge/L2单词表/情境分类.xlsx
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business_knowledge/L2单词表/情境分类.xlsx
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STEP1_LEARNING_FLOW = r"""你是一名儿童英语学习助手，负责为学习者生成互动式的巩固练习题。请根据以下信息生成练习内容：
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**输入信息**
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$任务信息$
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$学习过程$
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**生成要求**
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1. 为每个【通关知识】的知识词汇生成一个小题，包含以下结构：
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1. 为每个【学习过程】中的每个单词生成一个小题，包含以下结构：
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```
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# 小题 X
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@ -75,10 +75,6 @@ Brainy：没错！"奖牌"对应的英文词汇是"medal"，你的拼写完全
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Brainy：太棒了！你复习了所有的关键知识点，拼对了每一个词，相信下一次你一定可以做得更好！
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```
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#输入
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任务信息：
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$任务信息$
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# 输出
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现在请按照上述信息和要求，直接输出目标内容。"""
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@ -2019,6 +2019,7 @@ def parse_core_writing_imgmakeword_fields(teaching_config):
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通关知识 = fields.get("通关知识", "")
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前置对话 = fields.get("前置对话", "")
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互动问题 = fields.get("互动问题", "")
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学习过程 = fields.get("学习过程", "")
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# 构建任务信息（composite）
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任务信息_parts = []
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@ -2039,6 +2040,7 @@ def parse_core_writing_imgmakeword_fields(teaching_config):
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"通关知识": 通关知识,
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"前置对话": 前置对话,
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"互动问题": 互动问题,
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"学习过程": 学习过程,
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"任务信息": 任务信息,
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}
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@ -2082,7 +2084,7 @@ def generate_core_writing_imgmakeword(cId, teaching_config, character_map=None,
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# ── Step 1: 学习流程配置 ──
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logger.info(f"[core_writing_imgMakeWord] Step 1/7: 学习流程配置")
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prompt1 = build_writing_imgmakeword_prompt("step1_learning_flow", {
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"任务信息": fields["任务信息"],
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"学习过程": fields["学习过程"],
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})
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学习流程配置, usage1 = llm_client.call("", prompt1, max_tokens=8192, temperature=0.3)
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学习流程配置 = 学习流程配置.strip()
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@ -27,6 +27,7 @@ PUSH_FIELD_MAP = {
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"core_writing_imgWrite": ["taskInfo", "textInfo", "studyInfo", "evalInfo"],
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"core_writing_questionMakeSentence": ["taskInfo", "textInfo", "studyInfo", "evalInfo"],
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"core_writing_questionWrite": ["taskInfo", "textInfo", "studyInfo", "evalInfo"],
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"core_writing_imgMakeWord": ["taskInfo", "preDialog", "imageInfo", "questionList", "studyInfo"],
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}
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ALL_CORE_FIELDS = [
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24
check_3034_structure.py
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check_3034_structure.py
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import pandas as pd
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file3034 = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/30-34.xlsx'
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df3034 = pd.read_excel(file3034)
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print("="*60)
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print("30-34.xlsx 文件结构：")
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print(f"行数: {len(df3034)}")
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print(f"列数: {len(df3034.columns)}")
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print("\n列名:")
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for i, col in enumerate(df3034.columns):
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print(f" 列{i}: {col}")
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print("\n前30行内容：")
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print(df3034.head(30))
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print("\n尝试找出包含单词的列...")
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# 检查前几列中包含字母的内容
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for col_idx in range(min(5, len(df3034.columns))):
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col_data = df3034.iloc[:, col_idx].dropna()
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# 统计包含字母的单元格数
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has_letter = col_data.apply(lambda x: any(c.isalpha() for c in str(x))).sum()
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print(f"列{col_idx}: 包含字母的单元格数 = {has_letter}")
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check_all_words.py
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check_all_words.py
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import pandas as pd
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# 文件路径
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file3034 = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/30-34.xlsx'
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fileL2 = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L2知识库-三级+A2.xlsx'
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# 读取文件
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df3034 = pd.read_excel(file3034)
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dfL2 = pd.read_excel(fileL2)
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print(f"30-34单词表: {len(df3034)} 个单词")
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print(f"L2知识库: {len(dfL2)} 个单词")
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# 创建L2单词查找集合
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l2_words = set(str(word).strip().lower() for word in dfL2.iloc[:, 0])
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# 检查每个单词
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missing_words = []
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for idx, row in df3034.iterrows():
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word = str(row.iloc[0]).strip().lower()
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if word not in l2_words:
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missing_words.append(row.iloc[0])
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print("\n" + "="*60)
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if len(missing_words) == 0:
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print("✅ 所有30-34中的单词都在L2知识库中！")
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else:
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print(f"❌ 有 {len(missing_words)} 个单词不在L2知识库中：")
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for word in missing_words:
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print(f" - {word}")
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print("\n详细检查结果（前20个）：")
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for idx, row in df3034.head(20).iterrows():
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word = str(row.iloc[0]).strip().lower()
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status = "✅" if word in l2_words else "❌"
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print(f"{idx+1:3d}. {row.iloc[0]:20s} {status}")
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print("\n统计信息：")
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total = len(df3034)
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found = total - len(missing_words)
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print(f"总单词数: {total}")
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print(f"已在知识库: {found}")
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print(f"缺失单词: {len(missing_words)}")
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print(f"覆盖度: {found/total*100:.1f}%")
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45
check_all_words_v2.py
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45
check_all_words_v2.py
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import pandas as pd
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# 文件路径
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file3034 = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/30-34.xlsx'
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fileL2 = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L2知识库-三级+A2.xlsx'
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# 读取文件
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df3034 = pd.read_excel(file3034)
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dfL2 = pd.read_excel(fileL2)
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print(f"30-34单词表: {len(df3034)} 个单词")
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print(f"L2知识库: {len(dfL2)} 个单词")
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# 创建L2单词查找集合
|
||||
l2_words = set(str(word).strip().lower() for word in dfL2.iloc[:, 0])
|
||||
|
||||
# 检查每个单词（在列2"单词"中）
|
||||
missing_words = []
|
||||
for idx, row in df3034.iterrows():
|
||||
word = str(row['单词']).strip().lower()
|
||||
if word not in l2_words:
|
||||
missing_words.append(row['单词'])
|
||||
|
||||
print("\n" + "="*60)
|
||||
if len(missing_words) == 0:
|
||||
print("✅ 所有30-34中的单词都在L2知识库中！")
|
||||
else:
|
||||
print(f"❌ 有 {len(missing_words)} 个单词不在L2知识库中：")
|
||||
for word in missing_words:
|
||||
print(f" - {word}")
|
||||
|
||||
print("\n详细检查结果（前30个）：")
|
||||
for idx, row in df3034.head(30).iterrows():
|
||||
word = str(row['单词']).strip().lower()
|
||||
status = "✅" if word in l2_words else "❌"
|
||||
print(f"{idx+1:3d}. {str(row['单词']):30s} {status}")
|
||||
|
||||
print("\n统计信息：")
|
||||
total = len(df3034)
|
||||
found = total - len(missing_words)
|
||||
print(f"总单词数: {total}")
|
||||
print(f"已在知识库: {found}")
|
||||
print(f"缺失单词: {len(missing_words)}")
|
||||
print(f"覆盖度: {found/total*100:.1f}%")
|
||||
30
check_available.py
Normal file
30
check_available.py
Normal file
@ -0,0 +1,30 @@
|
||||
|
||||
import pandas as pd
|
||||
|
||||
file_l2 = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L2新版独有单词.xlsx'
|
||||
file_kb = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L2知识库-三级+A2.xlsx'
|
||||
|
||||
df_l2 = pd.read_excel(file_l2)
|
||||
df_kb = pd.read_excel(file_kb)
|
||||
|
||||
print("=== L2新版独有单词 ===")
|
||||
print(f"行数: {len(df_l2)}, 列数: {len(df_l2.columns)}")
|
||||
print(f"列名: {list(df_l2.columns)}")
|
||||
print(df_l2.head(3).to_string())
|
||||
print()
|
||||
|
||||
print("=== L2知识库-三级+A2 ===")
|
||||
print(f"行数: {len(df_kb)}, 列数: {len(df_kb.columns)}")
|
||||
print(f"列名: {list(df_kb.columns)}")
|
||||
print(df_kb.head(3).to_string())
|
||||
print()
|
||||
|
||||
# 查看"可用"列的内容
|
||||
if '可用' in df_l2.columns:
|
||||
print("=== 可用列的值分布 ===")
|
||||
print(df_l2['可用'].value_counts())
|
||||
print()
|
||||
# 找出可用=1的行
|
||||
available = df_l2[df_l2['可用'] == 1]
|
||||
print(f"可用=1的行数: {len(available)}")
|
||||
print(available.to_string())
|
||||
43
check_final.py
Normal file
43
check_final.py
Normal file
@ -0,0 +1,43 @@
|
||||
|
||||
import pandas as pd
|
||||
|
||||
# 文件路径
|
||||
file3034 = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/30-34.xlsx'
|
||||
fileL2 = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L2知识库-三级+A2.xlsx'
|
||||
|
||||
# 读取文件
|
||||
df3034 = pd.read_excel(file3034)
|
||||
dfL2 = pd.read_excel(fileL2)
|
||||
|
||||
print(f"30-34单词表: {len(df3034)} 个单词")
|
||||
print(f"L2知识库: {len(dfL2)} 个单词")
|
||||
|
||||
# 创建L2单词查找集合
|
||||
l2_words = set(str(word).strip().lower() for word in dfL2.iloc[:, 0])
|
||||
|
||||
# 检查每个单词
|
||||
missing_words = []
|
||||
for idx, row in df3034.iterrows():
|
||||
word = str(row['单词']).strip().lower()
|
||||
if word not in l2_words:
|
||||
missing_words.append(row)
|
||||
|
||||
print("\n" + "="*60)
|
||||
if len(missing_words) == 0:
|
||||
print("✅ 所有30-34中的单词都在L2知识库中！")
|
||||
else:
|
||||
print(f"❌ 还有 {len(missing_words)} 个单词不在L2知识库中：")
|
||||
print("-"*60)
|
||||
print(f"{'单词':<30} {'词性':<10} {'中文释义'}")
|
||||
print("-"*60)
|
||||
for row in missing_words:
|
||||
print(f"{str(row['单词']):<30} {str(row['词性']):<10} {str(row['释义'])}")
|
||||
|
||||
print("\n" + "="*60)
|
||||
print("统计信息：")
|
||||
total = len(df3034)
|
||||
found = total - len(missing_words)
|
||||
print(f"总单词数: {total}")
|
||||
print(f"已在知识库: {found}")
|
||||
print(f"缺失单词: {len(missing_words)}")
|
||||
print(f"覆盖度: {found/total*100:.1f}%")
|
||||
14
check_kb_structure.py
Normal file
14
check_kb_structure.py
Normal file
@ -0,0 +1,14 @@
|
||||
|
||||
import pandas as pd
|
||||
|
||||
file_kb = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L2知识库-三级+A2.xlsx'
|
||||
df = pd.read_excel(file_kb)
|
||||
|
||||
print(f"行数: {len(df)}, 列数: {len(df.columns)}")
|
||||
print(f"列名: {list(df.columns)}")
|
||||
print()
|
||||
print("前5行:")
|
||||
print(df.head(5).to_string())
|
||||
print()
|
||||
print("后5行:")
|
||||
print(df.tail(5).to_string())
|
||||
21
check_result.py
Normal file
21
check_result.py
Normal file
@ -0,0 +1,21 @@
|
||||
|
||||
import pandas as pd
|
||||
|
||||
outputFile = r'/root/.openclaw/workspace-xiaoyan/output/30-34标注完成.xlsx'
|
||||
|
||||
# 读取结果文件
|
||||
df = pd.read_excel(outputFile)
|
||||
|
||||
print("=== 结果文件样例 ===")
|
||||
print(f"列名: {df.columns.tolist()}")
|
||||
print(f"\n前20行数据:")
|
||||
print(df.head(20))
|
||||
|
||||
# 统计一下
|
||||
yes_count = (df['是否三级'].str.contains('【是】')).sum()
|
||||
no_count = len(df) - yes_count
|
||||
|
||||
print(f"\n\n=== 统计结果 ===")
|
||||
print(f"总单词数: {len(df)}")
|
||||
print(f"是三级词汇: {yes_count}")
|
||||
print(f"不是三级词汇: {no_count}")
|
||||
19
check_structures.py
Normal file
19
check_structures.py
Normal file
@ -0,0 +1,19 @@
|
||||
|
||||
import pandas as pd
|
||||
|
||||
file_l2 = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L2新版独有单词.xlsx'
|
||||
file_l1 = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L1完整.xlsx'
|
||||
|
||||
df_l2 = pd.read_excel(file_l2)
|
||||
df_l1 = pd.read_excel(file_l1)
|
||||
|
||||
print("=== L2新版独有单词 ===")
|
||||
print(f"行数: {len(df_l2)}, 列数: {len(df_l2.columns)}")
|
||||
print(f"列名: {list(df_l2.columns)}")
|
||||
print(df_l2.head(5).to_string())
|
||||
print()
|
||||
|
||||
print("=== L1完整 ===")
|
||||
print(f"行数: {len(df_l1)}, 列数: {len(df_l1.columns)}")
|
||||
print(f"列名: {list(df_l1.columns)}")
|
||||
print(df_l1.head(5).to_string())
|
||||
48
classify_l2.py
Normal file
48
classify_l2.py
Normal file
File diff suppressed because one or more lines are too long
71
compare_l1_l2.py
Normal file
71
compare_l1_l2.py
Normal file
@ -0,0 +1,71 @@
|
||||
|
||||
import pandas as pd
|
||||
|
||||
# 文件路径
|
||||
l2_file = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L2知识库-三级+A2.xlsx'
|
||||
l1_file = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L1完整.xlsx'
|
||||
|
||||
# 读取文件
|
||||
l2_df = pd.read_excel(l2_file)
|
||||
l1_df = pd.read_excel(l1_file)
|
||||
|
||||
print("L2文件列名:", l2_df.columns.tolist())
|
||||
print(f"L2行数: {len(l2_df)}")
|
||||
print("\nL1文件列名:", l1_df.columns.tolist())
|
||||
print(f"L1行数: {len(l1_df)}")
|
||||
|
||||
# 创建L1单词查找字典（小写为key）
|
||||
l1_dict = {}
|
||||
for idx, row in l1_df.iterrows():
|
||||
word = str(row.iloc[0]).strip().lower()
|
||||
pos = row.iloc[1] if len(row) > 1 else ""
|
||||
meaning = row.iloc[2] if len(row) > 2 else ""
|
||||
l1_dict[word] = {
|
||||
"row": idx + 2, # Excel从第2行开始
|
||||
"pos": pos,
|
||||
"meaning": meaning
|
||||
}
|
||||
|
||||
# 处理L2表格
|
||||
is_l1 = []
|
||||
l1_rows = []
|
||||
l1_pos = []
|
||||
l1_meaning = []
|
||||
|
||||
for idx, row in l2_df.iterrows():
|
||||
word = str(row.iloc[0]).strip().lower()
|
||||
if word in l1_dict:
|
||||
info = l1_dict[word]
|
||||
is_l1.append("是")
|
||||
l1_rows.append(info["row"])
|
||||
l1_pos.append(info["pos"])
|
||||
l1_meaning.append(info["meaning"])
|
||||
else:
|
||||
is_l1.append("否")
|
||||
l1_rows.append("")
|
||||
l1_pos.append("")
|
||||
l1_meaning.append("")
|
||||
|
||||
# 添加新列
|
||||
l2_df["是否为L1单词"] = is_l1
|
||||
l2_df["在L1中的行数"] = l1_rows
|
||||
l2_df["L1词性"] = l1_pos
|
||||
l2_df["L1词义"] = l1_meaning
|
||||
|
||||
# 保存结果
|
||||
l2_df.to_excel(l2_file, index=False)
|
||||
|
||||
# 统计
|
||||
count_yes = is_l1.count("是")
|
||||
count_no = is_l1.count("否")
|
||||
|
||||
print("\n" + "="*50)
|
||||
print(f"比对完成！")
|
||||
print(f"L1单词总数: {len(l1_df)}")
|
||||
print(f"L2单词总数: {len(l2_df)}")
|
||||
print(f"重复单词数: {count_yes}")
|
||||
print(f"不重复单词数: {count_no}")
|
||||
print(f"重复率: {count_yes/len(l2_df)*100:.2f}%")
|
||||
print("="*50)
|
||||
|
||||
print("\n保存成功！已更新 L2知识库-三级+A2.xlsx")
|
||||
31
compare_new_vs_old.py
Normal file
31
compare_new_vs_old.py
Normal file
@ -0,0 +1,31 @@
|
||||
|
||||
import pandas as pd
|
||||
|
||||
file_new = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L2新版_L1重复标记.xlsx'
|
||||
file_old = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L2知识库-三级+A2.xlsx'
|
||||
|
||||
df_new = pd.read_excel(file_new)
|
||||
df_old = pd.read_excel(file_old)
|
||||
|
||||
old_words = set(str(w).strip().lower() for w in df_old.iloc[:, 0])
|
||||
|
||||
only_in_new = []
|
||||
for idx, row in df_new.iterrows():
|
||||
word = str(row['单词']).strip().lower()
|
||||
if word not in old_words:
|
||||
only_in_new.append(row)
|
||||
|
||||
print(f"L2新版总单词数: {len(df_new)}")
|
||||
print(f"L2知识库总单词数: {len(df_old)}")
|
||||
print(f"\nL2新版中存在但L2知识库中不存在的单词: {len(only_in_new)} 个")
|
||||
|
||||
# 保存到文件
|
||||
with open('/root/.openclaw/workspace-xiaoyan/only_in_new.txt', 'w', encoding='utf-8') as f:
|
||||
f.write(f"L2新版中存在但L2知识库中不存在的单词: {len(only_in_new)} 个\n")
|
||||
f.write("=" * 60 + "\n")
|
||||
f.write(f"{'序号':<5} {'单词':<35} {'词性':<12} {'词义'}\n")
|
||||
f.write("-" * 80 + "\n")
|
||||
for i, row in enumerate(only_in_new, 1):
|
||||
f.write(f"{i:<5} {str(row['单词']):<35} {str(row['词性']):<12} {str(row['词义'])}\n")
|
||||
|
||||
print("\n完整列表已保存到 only_in_new.txt")
|
||||
@ -1,41 +0,0 @@
|
||||
import pandas as pd
|
||||
|
||||
# 文件路径
|
||||
file1_path = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/新知识库初版/全包词汇/L2知识库-三级+A2.xlsx'
|
||||
file2_path = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/新知识库初版/全包词汇/KET和三级不重复.xlsx'
|
||||
|
||||
# 读取两个文件
|
||||
df1 = pd.read_excel(file1_path)
|
||||
df2 = pd.read_excel(file2_path)
|
||||
|
||||
print("文件1 (L2知识库-三级+A2.xlsx) 的列名：")
|
||||
print(df1.columns.tolist())
|
||||
print("\n文件1 的前5行：")
|
||||
print(df1.head())
|
||||
|
||||
print("\n" + "="*80 + "\n")
|
||||
|
||||
print("文件2 (KET和三级不重复.xlsx) 的列名：")
|
||||
print(df2.columns.tolist())
|
||||
print("\n文件2 的前5行：")
|
||||
print(df2.head())
|
||||
|
||||
# 提取文件1中的单词（第一列）
|
||||
words_from_file1 = set(df1.iloc[:, 0].astype(str).str.strip().str.lower())
|
||||
|
||||
# 在文件2中新增一列，标记重复的单词
|
||||
df2['是否与L2三级+A2重复'] = df2.iloc[:, 0].apply(lambda x: '是' if str(x).strip().lower() in words_from_file1 else '')
|
||||
|
||||
# 统计重复数量
|
||||
duplicate_count = (df2['是否与L2三级+A2重复'] == '是').sum()
|
||||
total_count = len(df2)
|
||||
|
||||
print(f"\n" + "="*80)
|
||||
print(f"总单词数：{total_count}")
|
||||
print(f"重复单词数：{duplicate_count}")
|
||||
print(f"重复率：{duplicate_count/total_count*100:.2f}%")
|
||||
|
||||
# 保存结果
|
||||
df2.to_excel(file2_path, index=False)
|
||||
print(f"\n已更新文件：{file2_path}")
|
||||
print("新增列名：是否与L2三级+A2重复")
|
||||
9
dump_all_words.py
Normal file
9
dump_all_words.py
Normal file
@ -0,0 +1,9 @@
|
||||
|
||||
import pandas as pd
|
||||
|
||||
file_kb = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L2知识库-三级+A2.xlsx'
|
||||
df = pd.read_excel(file_kb)
|
||||
|
||||
# 查看所有单词和词义
|
||||
for i, row in df.iterrows():
|
||||
print(f"{i}: {str(row['单词']):<30} {str(row['词性']):<10} {str(row['中文释义'])}")
|
||||
12
dump_words.py
Normal file
12
dump_words.py
Normal file
@ -0,0 +1,12 @@
|
||||
|
||||
import pandas as pd
|
||||
|
||||
file_kb = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L2知识库-三级+A2.xlsx'
|
||||
df = pd.read_excel(file_kb)
|
||||
|
||||
# 查看所有单词，保存到文件以便分析
|
||||
with open('/root/.openclaw/workspace-xiaoyan/all_words_dump.txt', 'w', encoding='utf-8') as f:
|
||||
for i, row in df.iterrows():
|
||||
f.write(f"{i}\t{str(row['单词'])}\t{str(row['词性'])}\t{str(row['中文释义'])}\n")
|
||||
|
||||
print(f"已导出 {len(df)} 个单词到 all_words_dump.txt")
|
||||
30
export_only_in_new.py
Normal file
30
export_only_in_new.py
Normal file
@ -0,0 +1,30 @@
|
||||
|
||||
import pandas as pd
|
||||
|
||||
file_new = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L2新版_L1重复标记.xlsx'
|
||||
file_old = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L2知识库-三级+A2.xlsx'
|
||||
|
||||
df_new = pd.read_excel(file_new)
|
||||
df_old = pd.read_excel(file_old)
|
||||
|
||||
old_words = set(str(w).strip().lower() for w in df_old.iloc[:, 0])
|
||||
|
||||
only_in_new = []
|
||||
for idx, row in df_new.iterrows():
|
||||
word = str(row['单词']).strip().lower()
|
||||
if word not in old_words:
|
||||
only_in_new.append({
|
||||
'单词': row['单词'],
|
||||
'词性': row['词性'],
|
||||
'词义': row['词义']
|
||||
})
|
||||
|
||||
df_result = pd.DataFrame(only_in_new)
|
||||
|
||||
output_path = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L2新版独有单词.xlsx'
|
||||
df_result.to_excel(output_path, index=False)
|
||||
|
||||
print(f"L2新版总单词数: {len(df_new)}")
|
||||
print(f"L2知识库总单词数: {len(df_old)}")
|
||||
print(f"L2新版中存在但L2知识库中不存在的单词: {len(df_result)} 个")
|
||||
print(f"\n已保存至: {output_path}")
|
||||
29
find_music_words.py
Normal file
29
find_music_words.py
Normal file
@ -0,0 +1,29 @@
|
||||
|
||||
import pandas as pd
|
||||
|
||||
file_path = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L2知识库-三级+A2.xlsx'
|
||||
df = pd.read_excel(file_path)
|
||||
|
||||
music_keywords = ['唱歌', '音乐', '歌曲', '歌', '唱', '乐器', '钢琴', '吉他', '小提琴', '鼓',
|
||||
'演奏', '乐队', '音乐会', '演唱会', '旋律', '节奏', '音符', '录音', '歌手',
|
||||
'sing', 'song', 'music', 'musical', 'piano', 'guitar', 'violin', 'drum',
|
||||
'band', 'concert', 'melody', 'rhythm', 'note', 'record', 'singer', 'musician',
|
||||
'instrument', 'listen', 'sound', 'voice', 'choir', 'opera', 'jazz',
|
||||
'rock', 'pop', 'dance', 'rap', 'tune', 'lyric', 'album', 'audio']
|
||||
|
||||
results = []
|
||||
for idx, row in df.iterrows():
|
||||
word = str(row.iloc[0]).strip().lower()
|
||||
meaning = str(row.iloc[2]).strip().lower() if len(row) > 2 else ''
|
||||
for kw in music_keywords:
|
||||
if kw in word or kw in meaning:
|
||||
results.append(row)
|
||||
break
|
||||
|
||||
print(f"L2知识库总单词数: {len(df)}")
|
||||
print(f"\n🎵 唱歌/音乐相关单词 ({len(results)} 个):")
|
||||
print("-" * 60)
|
||||
print(f"{'单词':<25} {'词性':<10} {'中文释义'}")
|
||||
print("-" * 60)
|
||||
for row in sorted(results, key=lambda x: str(x.iloc[0]).lower()):
|
||||
print(f"{str(row.iloc[0]):<25} {str(row.iloc[1]):<10} {str(row.iloc[2])}")
|
||||
54
mark_l1_in_l2new.py
Normal file
54
mark_l1_in_l2new.py
Normal file
@ -0,0 +1,54 @@
|
||||
|
||||
import pandas as pd
|
||||
|
||||
file_l2 = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L2新版独有单词.xlsx'
|
||||
file_l1 = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L1完整.xlsx'
|
||||
|
||||
df_l2 = pd.read_excel(file_l2)
|
||||
df_l1 = pd.read_excel(file_l1)
|
||||
|
||||
l1_words = {}
|
||||
for idx, row in df_l1.iterrows():
|
||||
word = str(row['单词']).strip().lower()
|
||||
if word not in l1_words:
|
||||
l1_words[word] = []
|
||||
l1_words[word].append({
|
||||
'row': idx + 2,
|
||||
'词性': row['词性'],
|
||||
'词义': row['词义']
|
||||
})
|
||||
|
||||
is_l1_list = []
|
||||
l1_row_list = []
|
||||
l1_pos_list = []
|
||||
l1_meaning_list = []
|
||||
|
||||
for idx, row in df_l2.iterrows():
|
||||
word = str(row['单词']).strip().lower()
|
||||
if word in l1_words:
|
||||
is_l1_list.append('是')
|
||||
matches = l1_words[word]
|
||||
l1_row_list.append(', '.join(str(m['row']) for m in matches))
|
||||
l1_pos_list.append(', '.join(str(m['词性']) for m in matches))
|
||||
l1_meaning_list.append(', '.join(str(m['词义']) for m in matches))
|
||||
else:
|
||||
is_l1_list.append('否')
|
||||
l1_row_list.append('')
|
||||
l1_pos_list.append('')
|
||||
l1_meaning_list.append('')
|
||||
|
||||
df_l2['是否为L1单词'] = is_l1_list
|
||||
df_l2['L1行数'] = l1_row_list
|
||||
df_l2['L1词性'] = l1_pos_list
|
||||
df_l2['L1词义'] = l1_meaning_list
|
||||
|
||||
output_path = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/L2新版独有单词.xlsx'
|
||||
df_l2.to_excel(output_path, index=False)
|
||||
|
||||
count_yes = sum(1 for v in is_l1_list if v == '是')
|
||||
count_no = sum(1 for v in is_l1_list if v == '否')
|
||||
|
||||
print(f"L2新版独有单词总数: {len(df_l2)}")
|
||||
print(f"在L1中存在的: {count_yes} 个")
|
||||
print(f"不在L1中的: {count_no} 个")
|
||||
print(f"\n已保存至: {output_path}")
|
||||
@ -95,3 +95,9 @@
|
||||
{"type":"memory.recall.recorded","timestamp":"2026-06-05T03:37:55.029Z","query":"武钰涵 ou_a4aaa641585b38d1042608b9b7f05a17","resultCount":1,"results":[{"path":"memory/2026-05-27.md","startLine":1,"endLine":16,"score":1}]}
|
||||
{"type":"memory.recall.recorded","timestamp":"2026-06-08T02:24:34.295Z","query":"L2 听力 题型 能力标签 Flyers KET 单元分布","resultCount":2,"results":[{"path":"memory/2026-05-25.md","startLine":283,"endLine":302,"score":1},{"path":"memory/2026-05-25.md","startLine":118,"endLine":137,"score":1}]}
|
||||
{"type":"memory.recall.recorded","timestamp":"2026-06-08T02:24:43.647Z","query":"L2 听力题型 能力标签 ability tag listening 图片选择 表格填空 长对话 短对话 信息匹配","resultCount":2,"results":[{"path":"memory/2026-05-25.md","startLine":283,"endLine":302,"score":1},{"path":"memory/2026-05-25.md","startLine":118,"endLine":137,"score":1}]}
|
||||
{"type":"memory.recall.recorded","timestamp":"2026-06-09T06:52:09.582Z","query":"雨橦 user_id 飞书","resultCount":2,"results":[{"path":"memory/2026-05-07.md","startLine":57,"endLine":74,"score":1},{"path":"memory/2026-05-07.md","startLine":71,"endLine":88,"score":1}]}
|
||||
{"type":"memory.recall.recorded","timestamp":"2026-06-09T07:14:02.277Z","query":"组件生产 中互动 问题 优化 上下文对话 前置后置","resultCount":2,"results":[{"path":"memory/2026-05-07.md","startLine":354,"endLine":368,"score":1},{"path":"memory/2026-04-22.md","startLine":1,"endLine":8,"score":1}]}
|
||||
{"type":"memory.recall.recorded","timestamp":"2026-06-09T07:21:33.704Z","query":"互动内容库 bitable app_token 配置多维表格","resultCount":4,"results":[{"path":"memory/2026-05-12.md","startLine":61,"endLine":80,"score":1},{"path":"memory/2026-05-18.md","startLine":305,"endLine":346,"score":1},{"path":"memory/2026-05-12.md","startLine":101,"endLine":121,"score":1},{"path":"memory/2026-05-18.md","startLine":783,"endLine":810,"score":1}]}
|
||||
{"type":"memory.recall.recorded","timestamp":"2026-06-09T07:22:27.500Z","query":"互动内容库 app_token 多维表格 bitable 配置","resultCount":4,"results":[{"path":"memory/2026-05-12.md","startLine":61,"endLine":80,"score":1},{"path":"memory/2026-05-18.md","startLine":305,"endLine":346,"score":1},{"path":"memory/2026-05-12.md","startLine":101,"endLine":121,"score":1},{"path":"memory/2026-05-18.md","startLine":783,"endLine":810,"score":1}]}
|
||||
{"type":"memory.recall.recorded","timestamp":"2026-06-09T08:37:12.232Z","query":"core_speaking_image bitable app token 多维表格","resultCount":3,"results":[{"path":"memory/2026-05-12.md","startLine":164,"endLine":187,"score":1},{"path":"memory/2026-05-12.md","startLine":214,"endLine":236,"score":1},{"path":"memory/2026-05-12.md","startLine":234,"endLine":254,"score":1}]}
|
||||
{"type":"memory.recall.recorded","timestamp":"2026-06-09T08:37:27.121Z","query":"core_speaking_image bitable_token app_token 多维表格 核心互动","resultCount":3,"results":[{"path":"memory/2026-05-12.md","startLine":164,"endLine":187,"score":1},{"path":"memory/2026-05-12.md","startLine":214,"endLine":236,"score":1},{"path":"memory/2026-05-12.md","startLine":234,"endLine":254,"score":1}]}
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
{
|
||||
"version": 1,
|
||||
"updatedAt": "2026-06-08T02:24:43.647Z",
|
||||
"updatedAt": "2026-06-09T08:37:27.121Z",
|
||||
"entries": {
|
||||
"memory:memory/2026-05-07.md:57:74": {
|
||||
"key": "memory:memory/2026-05-07.md:57:74",
|
||||
@ -9,20 +9,22 @@
|
||||
"endLine": 74,
|
||||
"source": "memory",
|
||||
"snippet": "3. **第三轮（用户要求重新发送三份文件后重算）：** 最终确认 **表1有但表2表3都没有 = 255个** - **第三轮详细统计：** - 三表共有：230 - 表1 ∩ 表2：492 - 表1 ∩ 表3：448 - 表2 ∩ 表3：427 - 表1有，表2没有：472（其中217个表3也有） - 最终：255个 - **关键发现：** - 表1（分类词表）覆盖了大量基础生活词汇，表3（L1词表）覆盖了其中的448个 - 最终的255个独有词主要集中在：抽象名词（advantage, attitude, courage）、学术/学科词汇（composition, grammar, pronunciation）、专业/特定领域词（cancer, clone, industry）等偏中高难度的词汇 - 表1和表3有大量重叠（448个），说明两个L1级别的词表覆盖范围高度一致 - 从上一轮260变成255（差5个），差异主要来自表2/表3重新解析后个别单词的增删 - **输出文件：** `/root/.openclaw/workspace-xiaoyan/output/单词对比结果_最终版.xlsx` - Sheet 1：255个单词完整清单（序号+单词+中文释义） - Sheet 2：全部统计指标汇总 - **用户 user_id：** `58fd6864`（租户级） - **发送消息时踩坑记录：** ou_xxx 是 open_id 非 user_id，Bot 缺少 contact:user.id:readonly 权限",
|
||||
"recallCount": 2,
|
||||
"recallCount": 3,
|
||||
"dailyCount": 0,
|
||||
"groundedCount": 0,
|
||||
"totalScore": 1.6621249966506229,
|
||||
"totalScore": 2.662124996650623,
|
||||
"maxScore": 1,
|
||||
"firstRecalledAt": "2026-05-07T08:48:55.437Z",
|
||||
"lastRecalledAt": "2026-05-21T07:31:52.322Z",
|
||||
"lastRecalledAt": "2026-06-09T06:52:09.582Z",
|
||||
"queryHashes": [
|
||||
"c2c576894acd",
|
||||
"dca4eb1f24b7"
|
||||
"dca4eb1f24b7",
|
||||
"84038f5c3181"
|
||||
],
|
||||
"recallDays": [
|
||||
"2026-05-07",
|
||||
"2026-05-21"
|
||||
"2026-05-21",
|
||||
"2026-06-09"
|
||||
],
|
||||
"conceptTags": [
|
||||
"学术/学科词汇",
|
||||
@ -876,13 +878,13 @@
|
||||
"endLine": 368,
|
||||
"source": "memory",
|
||||
"snippet": "- **需求：** 将 020102（I am...）和 020103（I am ready / Thank you）两套题合并为一个 `{first:..., second:...}` JSON，统一 questionSetID=0000001 - **状态：** ✅ 已完成 - **核心考点分析（用户强调）：** 需分析每个句型的核心考点（孩子最容易犯错的地方），挖空对准核心考点 - I am/from 组：am（系动词第一人称）、from（介词选择）、student（a+名词结构） - Thank you for 组：for（介词选择，非 you）、helping（for+动名词，非 help/to help） - **输出文件：** `output/writing_pic_qa_combined.json` ### 刘彦江 — 组件配置-json 请求（L1-S2-U13-L4 沙漠之花） - **时间：** 16:45 ~ 17:51 - **文档：** `https://makee-interactive.feishu.cn/wiki/K5E1wzwk7it9t7kXvcbc6Xugnhc` - **状态：** ⚠️ 未完成 — pipeline 识别到 0 组件 - **根因：** 剧本文档的13个组件数据存储在 markdown 内联表格中（lark-table，5列×36行），而非内嵌 Sheet。当前 pipeline 的 parse_script 只从内嵌 Sheet 读取组件数据，不支持 markdown 表格组件解析 - **已识别组件（ma",
|
||||
"recallCount": 15,
|
||||
"recallCount": 16,
|
||||
"dailyCount": 0,
|
||||
"groundedCount": 0,
|
||||
"totalScore": 15,
|
||||
"totalScore": 16,
|
||||
"maxScore": 1,
|
||||
"firstRecalledAt": "2026-05-15T07:13:08.147Z",
|
||||
"lastRecalledAt": "2026-06-04T06:34:06.417Z",
|
||||
"lastRecalledAt": "2026-06-09T07:14:02.277Z",
|
||||
"queryHashes": [
|
||||
"08364c8746ab",
|
||||
"4f08741ab4fd",
|
||||
@ -898,7 +900,8 @@
|
||||
"f53c9769e692",
|
||||
"5d025e291984",
|
||||
"472b243ec77c",
|
||||
"fd4c8aca2cb6"
|
||||
"fd4c8aca2cb6",
|
||||
"5319f16d7a0d"
|
||||
],
|
||||
"recallDays": [
|
||||
"2026-05-15",
|
||||
@ -908,7 +911,8 @@
|
||||
"2026-05-25",
|
||||
"2026-05-28",
|
||||
"2026-06-01",
|
||||
"2026-06-04"
|
||||
"2026-06-04",
|
||||
"2026-06-09"
|
||||
],
|
||||
"conceptTags": [
|
||||
"am/from",
|
||||
@ -928,13 +932,13 @@
|
||||
"endLine": 8,
|
||||
"source": "memory",
|
||||
"snippet": "[李应瑛 2026-04-22 提出要求] 所有需要包含对话的内容（如剧本、互动组件等）必须要有【后置对话】字段，无后置对话时填写“无”。 [李应瑛 2026-04-22 确认规则] 剧本内嵌表格组件填写位置规则：仅当表格第一列（A列）明确标注为对话类类型（对话朗读/对话挖空/对话选读/对话组句等）时，才在同一行的H列（【组件】列）填写对应的组件内容，其他类型行（TL/场景/角色/图片/非对话类等）无需填写。 [李应瑛 2026-04-22 确认格式规则] 对话类组件字段换行规则：每个结构单独占一行，格式为： 【任务标题】xxx 【情境引入】xxx 【互动内容】xxx 【后置对话】xxx 单元格内使用\\n作为换行符实现，后续所有组件均遵循此格式。",
|
||||
"recallCount": 13,
|
||||
"recallCount": 14,
|
||||
"dailyCount": 0,
|
||||
"groundedCount": 0,
|
||||
"totalScore": 13,
|
||||
"totalScore": 14,
|
||||
"maxScore": 1,
|
||||
"firstRecalledAt": "2026-05-15T07:13:08.147Z",
|
||||
"lastRecalledAt": "2026-06-04T06:34:06.417Z",
|
||||
"lastRecalledAt": "2026-06-09T07:14:02.277Z",
|
||||
"queryHashes": [
|
||||
"08364c8746ab",
|
||||
"4f08741ab4fd",
|
||||
@ -948,7 +952,8 @@
|
||||
"229e5d3943bb",
|
||||
"5d025e291984",
|
||||
"472b243ec77c",
|
||||
"fd4c8aca2cb6"
|
||||
"fd4c8aca2cb6",
|
||||
"5319f16d7a0d"
|
||||
],
|
||||
"recallDays": [
|
||||
"2026-05-15",
|
||||
@ -958,7 +963,8 @@
|
||||
"2026-05-25",
|
||||
"2026-05-28",
|
||||
"2026-06-01",
|
||||
"2026-06-04"
|
||||
"2026-06-04",
|
||||
"2026-06-09"
|
||||
],
|
||||
"conceptTags": [
|
||||
"对话朗读/对话挖空/对话选读/对话组句等",
|
||||
@ -1756,20 +1762,23 @@
|
||||
"endLine": 346,
|
||||
"source": "memory",
|
||||
"snippet": "## 口语-P1 审校（[刘彦江]） ### 范围 QSID: 121301, 121401, 121501（口语-P1-日常回答，tblRGv7k4WH58Jgq） ### 发现问题 - **121301**: 6题 ability 全空 - **121401**: ability 重复（\"听觉抓取关键信息\"×2）+ 标签偏向听力而非口语 - **121501**: 6题 ability 全空 - answer 为空 → 正常（speaking_qa 参考回答在 explanation 内） ### 修复 - 全部补充/替换为口语向标签（语用表达能力等） - 3/3 审校结果列更新为 `✅ OK | 2026-05-18 小研审校` ### Record ID 映射 | QSID | Record ID | |------|-----------| | 121301 | recvjW9RVwziwL | | 121401 | recvjW9RVwGzba | | 121501 | recvjW9RVwj4aQ | --- ## 听力-P1 图片选择题 生产+回填+审校（[刘彦江]） ### 范围 QSID: 121301, 121401, 121501（听力-P1-图片选择题） 难度: L1 B级 ### 词汇词库命中率 29个词中仅6个在L1/L2词库（sofa, zoo, study, play, mat, mouth），23个超纲。 业务负责人放行，优先生产，词库后补。 ### 产出 | QSID | Record ID | 词表(first/seco",
|
||||
"recallCount": 2,
|
||||
"recallCount": 4,
|
||||
"dailyCount": 0,
|
||||
"groundedCount": 0,
|
||||
"totalScore": 2,
|
||||
"totalScore": 4,
|
||||
"maxScore": 1,
|
||||
"firstRecalledAt": "2026-05-20T11:03:34.460Z",
|
||||
"lastRecalledAt": "2026-05-25T08:53:31.758Z",
|
||||
"lastRecalledAt": "2026-06-09T07:22:27.500Z",
|
||||
"queryHashes": [
|
||||
"81867276810d",
|
||||
"12653110bb68"
|
||||
"12653110bb68",
|
||||
"ff0d0637f255",
|
||||
"f525b14a0c5b"
|
||||
],
|
||||
"recallDays": [
|
||||
"2026-05-20",
|
||||
"2026-05-25"
|
||||
"2026-05-25",
|
||||
"2026-06-09"
|
||||
],
|
||||
"conceptTags": [
|
||||
"口语-p1",
|
||||
@ -1956,19 +1965,21 @@
|
||||
"endLine": 88,
|
||||
"source": "memory",
|
||||
"snippet": "- Sheet 1：255个单词完整清单（序号+单词+中文释义） - Sheet 2：全部统计指标汇总 - **用户 user_id：** `58fd6864`（租户级） - **发送消息时踩坑记录：** ou_xxx 是 open_id 非 user_id，Bot 缺少 contact:user.id:readonly 权限，最终通过 lark-cli 其他接口获取到 user_id = 58fd6864 - **备注：** 李应瑛不在负责人配置中，不在完整权限用户列表中。当前请求属于教研内容分析，已先接单处理。 # 2026-05-07 工作日志 ## 会话记录 ### 李应瑛 (ou_1bd7317ae2ccfeb57e1132028847279e) — 单词表三轮对比（已完结） - **时间：** 10:39 ~ 11:24 - **需求：** 对比三份单词表，找出\"表1有但表2和表3都没有\"的单词。 - **状态：** ✅ 已完成，结果已交付用户，最终结果 255 个单词，经多次验证（包括用户重新发送文件后重算）结果一致。 - **文件：** - 表1：.doc 文件（分类主题单词表，14个大类，970个单词） - 表2：.xlsx 文件（LV2词表，1,329个单词，含「LV2-单词」「LV2上单词占用情况」「LV2下单词占用情况」「LV2S3」四个sheet） - 表3：.xlsx 文件（L1词表，845个单词，含「单词表-LV1（上）」「单词表-LV1（下）」「单词表-备用」三个sheet合并去重） - **对比过程（共四轮验证）：**",
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|
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|
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@ -3156,6 +3167,198 @@
|
||||
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|
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|
||||
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|
||||
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|
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||||
"snippet": "- **脚本修复（过程中）：** 1. sentenceStructureSort 单词对比大小写误报 → 添加 normalize（首字母大写 + 去标点） 2. ID 末尾空格敏感 → strip 比较 3. 句子朗读/跟读题误报 options/answer 缺失 → 跳过这两种题型 #### 审校结果汇总 - **🔴 系统性问题（40/40，100%）：** classificationInfo JSON 内 cambridgeLevel=\"A1\" 与 cefrLevel=\"YLE\" 值互换，列字段正确但 JSON 存储写反，疑似模板/formula 级别 bug - **🟡 具体错误（17/40）：** - sentenceMeaningMeaning JSON 解析失败 × 10：1213004, 1213006, 1213010, 1214008, 1215005, 1216001, 1216004, 1216007, 1216008, 1216010（后8题含中文引号冲突） - ID 字段末尾空格 × 3：1213001, 1215001, 1216001 - 看图选择题有CN无JSON × 3：1213008, 1215010, 1216005 - sentenceStructureSort 语法/格式错误 × 3：1213004(缺逗号clean Otis→clean, Otis), 1213005(bird?连写应拆为bird+?), 1214001(need→needs三单) - 缺少 pointId 字段 × 1：",
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||||
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||||
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|
||||
"疑似模板/formula",
|
||||
"17/40",
|
||||
"语法/格式错误",
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||||
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||||
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||||
"snippet": "3. ⚠️ sentenceStructureClozeWordMcq: \"put your toys\" 省略 away 不够自然 - **技能沉淀：** - 脚本: `scripts/audit_l1_config.py` — 7项自动检查 - SKILL.md: `business_production/_shared/audit_l1_config/SKILL.md` — 含完整审校流程 + 4项人工审核清单 ### 刘彦江 — 1213001-1216010 批量审校 40 条（12:10 ~ 14:00） - **范围：** L1S2 Units 13-16，每单元 10 条，共 40 条 - **方法：** `scripts/audit_l1_config.py` 自动化扫描 → 误报排除 → 分类汇总 → 逐条审校回填 - **脚本修复（过程中）：** 1. sentenceStructureSort 单词对比大小写误报 → 添加 normalize（首字母大写 + 去标点） 2. ID 末尾空格敏感 → strip 比较 3. 句子朗读/跟读题误报 options/answer 缺失 → 跳过这两种题型 #### 审校结果汇总（初版） - **🔴 系统性问题（40/40，100%）：** classificationInfo JSON 内 cambridgeLevel=\"A1\" 与 cefrLevel=\"YLE\" 值互换 - **🟡 具体错误（17/40）：** - sentenceMeaningMeaning JSON 解析",
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"2026-06-09"
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],
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"conceptTags": [
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||||
"scripts/audit-l1-config.py",
|
||||
"skill.md",
|
||||
"business-production",
|
||||
"shared/audit-l1-config/skill.md",
|
||||
"1213001-1216010",
|
||||
"13-16",
|
||||
"句子朗读/跟读题误报",
|
||||
"options/answer"
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]
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"memory:memory/2026-05-18.md:783:810": {
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"startLine": 783,
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||||
"snippet": "--- ## [李应瑛] L1-S2-U17-L5 笨蛋坏蛋？英文生产（2026-05-18 21:15） ### 背景 李应瑛发送知识库文档 `U1zEwHZaaie07TkjmOOchurgnIh`（L1-S2-U17-L5 笨蛋坏蛋？），要求\"英文生产\"。 ### 文档结构（与L3/L4不同） - Wiki token: `U1zEwHZaaie07TkjmOOchurgnIh` - Spreadsheet token: `DCcKsLbrmhfXgrtB7N2c9GA4ntf` - Script sheet: `wMQVyV`（186行×9列） - Knowledge points sheet: `DCcKsLbrmhfXgrtB7N2c9GA4ntf_NtIcXt` - 列结构：A=类型, D=剧情描述, E=角色名, F=编剧台词（English已填好）, **G=组件配置** - 知识点：point, talk, understand, a lot of + `talk to...` / `I can/can't understand...` ### 交互模式差异 - L5 文档 B列/C列 为空，无详细组件类型标签（仅 A=互动/核心互动-口语） - 编剧台词F列已全部填写（英文） - 互动行的 User 台词含红色标注知识点词 ### 完成事项 - 24个互动行全部生成G列配置（组件类型推断：听力挖空、朗读台词、口语表达） - 写入方式：同上 Sheets v2 API - 24/24 全部回读验证通过 ### 脚本 `scripts/write_",
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"2026-06-09"
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],
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"conceptTags": [
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||||
"l1-s2-u17-l5",
|
||||
"与l3/l4不同",
|
||||
"can/can",
|
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"b列/c列",
|
||||
"互动/核心互动-口语",
|
||||
"24/24",
|
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"scripts/write",
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"u17"
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]
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"memory:memory/2026-05-12.md:164:187": {
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"snippet": "- **五大分类：** 📖阅读(2) / 🎧听力(3) / 🗣口语(4) / ✏️写作(5) / 未完成(1) - **通用字段提取：** ID/dataStatus/kpStatus/kpInfo/taskData 是所有题型共用的基础结构 #### 覆盖率 - 14 种题型已录入 ✅（1 种无数据：口语探讨） - 4 个一级板块：通用结构 / 题型专区 / 审校规则 / 扩展指南 - 每个题型：cType + bitable 定位 + JSON 字段表 + 结构特点 + 与同类题型的差异说明 #### 发现 - 写作互动和邮件组句 cType 相同（`core_writing_questionMakeSentence`），通过 textInfo 区分素材 - 口语快答/妙问 JSON 结构几乎相同，差异在 prompt 配置和对话样例内容 - 合作阅读和合作听力的核心差异：`textData.text[]` ↔ `textData.audio[]` - meaning 标签（合作阅读）为开放型自由文本，非受控词表 #### 产出文件 - `business_production/_shared/bitable-reader/SKILL.md` — 通用 bitable 读取技能（164行） - `business_knowledge/skills/core-content-json-standard/SKILL.md` — 全题型 JSON 标准 v2.0（393行） - `scripts/audit_core_reading_S0.py` — 合作阅读 S0",
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"口语快答/妙问",
|
||||
"textdata.text",
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"textdata.audio",
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"business-production",
|
||||
"shared/bitable-reader/skill.md",
|
||||
"v2.0",
|
||||
"scripts/audit-core-reading-s0.py",
|
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"五大"
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]
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|
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"snippet": "- **产出 Skill 2：** `business_knowledge/skills/core-content-json-standard/SKILL.md` v2.0 — 全题型 JSON 标准（393行） - **架构：** 通用字段在前（ID/kpInfo/taskData）+ 5大题型分类（📖阅读2 🎧听力3 🗣口语4 ✏️写作5）+ 审校规则 + 扩展指南 - **覆盖率：** 14/15 种题型（口语探讨 S0 无数据） - **产出脚本：** `scripts/audit_core_reading_S0.py` — 合作阅读 S0 审校 ## 经验教训 ### bitable 写入需严格流程管控（2026-05-12） - 批量更新 JSON：写入前完整提取 → 写入后逐字段 diff → 异常立即回滚 - 首次修复多题 JSON 导致 Q2 丢失 → 先单条试跑验证再批量 ### sentenceMeaningMeaning 引号规范（2026-05-12） - explanation 中 ASCII `\"` 与中文引号混用导致 JSON 解析失败 - 建议：生产侧统一用中文引号 `\"\"`（U+201C/D），或写入前做引号归一化 ### 写作互动 / 邮件组句 cType 相同（2026-05-12） - 两者 cType 均为 `core_writing_questionMakeSentence`，通过 textInfo.textList[].type 区分素材 ### 刘彦江 — 中互动全题型录入（18:00 ~ 18:25） ###",
|
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],
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"2026-06-09"
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||||
],
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"conceptTags": [
|
||||
"v2.0",
|
||||
"id/kpinfo/taskdata",
|
||||
"14/15",
|
||||
"scripts/audit-core-reading-s0.py",
|
||||
"201c/d",
|
||||
"textinfo.textlist",
|
||||
"skill",
|
||||
"business-knowledge"
|
||||
]
|
||||
},
|
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"memory:memory/2026-05-12.md:234:254": {
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"key": "memory:memory/2026-05-12.md:234:254",
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"path": "memory/2026-05-12.md",
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"startLine": 234,
|
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|
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"source": "memory",
|
||||
"snippet": "#### 发现 - **中互动结构高度统一：** 全部 27 类都用 `jsonData`（主配置）+ `kpInfo`（知识点绑定），没有核心互动那种多字段分散结构 - **6 大分类：** 🗨对话(7) / 📋信息(6) / 📦词汇(2) / 🖼图片选择(4) / 📝语法(3) / 🔧其他(5) - **对话类公共字段：** cType, cId, title, resourceMapping, preDialog, imgShowTimingList - 写作互动和邮件组句 cType 相同（`core_writing_questionMakeSentence`） #### 产出文件 - `skills/interactive-component-json/references/json_structure_guide.md` — 27 种中互动 jsonData 字段结构（8736 bytes） - `skills/interactive-component-json/references/component_registry.md` — 核心互动部分从🔲更新为完整注册表（含 cType+bitable_token） - 与 `business_knowledge/skills/core-content-json-standard/SKILL.md` 双向关联 #### 覆盖率 - 核心互动：15/15 ✅（口语探讨 S0 无数据已标注） - 中互动：27/27 ✅ - 关联文件：3 个（core-content-json-standard + com",
|
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"queryHashes": [
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"2026-06-09"
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],
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"conceptTags": [
|
||||
"bitable-token",
|
||||
"15/15",
|
||||
"27/27",
|
||||
"core-content-json-standard",
|
||||
"发现",
|
||||
"互动",
|
||||
"结构",
|
||||
"高度"
|
||||
]
|
||||
}
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}
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}
|
||||
|
||||
62
memory/2026-06-09-1159.md
Normal file
62
memory/2026-06-09-1159.md
Normal file
@ -0,0 +1,62 @@
|
||||
# Session: 2026-06-09 11:59:37 GMT+8
|
||||
|
||||
- **Session Key**: agent:xiaoyan:feishu:direct:ou_8b79b7f711e42a4004f3f3c225a1a99c
|
||||
- **Session ID**: 5e1b9add-de36-44b7-88c4-47ca70c8bb35
|
||||
- **Source**: feishu
|
||||
|
||||
## Conversation Summary
|
||||
|
||||
user: 组件配置-json https://makee-interactive.feishu.cn/wiki/SAadwW8YaiObl8k4op3c6vs8nqb
|
||||
assistant: 处理完成。**L1-S2-U17-L4 门关啦 Door is Closed** 共识别 **16 个组件**，全部生成成功：
|
||||
|
||||
| # | 组件ID | 类型 | cType |
|
||||
|---|--------|------|-------|
|
||||
| 1-3 | 1217401-1217403 | 对话朗读 | mid_dialog_repeat |
|
||||
| 4-5 | 1217404-1217405 | 对话挖空 | mid_dialog_fillin |
|
||||
| 6 | 1217406 | 图片单选 | mid_image_choose |
|
||||
| 7 | 1217407 | 对话朗读 | mid_dialog_repeat |
|
||||
| 8-9 | 1217408-1217409 | 对话选择 | mid_dialog_choose |
|
||||
| 10 | 1217410 | 对话挖空 | mid_dialog_fillin |
|
||||
| 11-12 | 1217411-1217412 | 对话组句 | mid_dialog_sentence |
|
||||
| 13 | 1217413 | 看图拼词 | core_writing_imgMakeWord |
|
||||
| 14-15 | 1217414-1217415 | 对话挖空 | mid_dialog_fillin |
|
||||
| 16 | 1217416 | 对话朗读 | mid_dialog_repeat |
|
||||
|
||||
✅ 成功 16 / 失败 0，HTML 报告已通过飞书 Bot 发送给你。
|
||||
user: https://makee-interactive.feishu.cn/wiki/U1zEwHZaaie07TkjmOOchurgnIh组件配置-json
|
||||
assistant: 处理完成。**L1-S2-U17-L5 笨蛋坏蛋？Bad Guy or Not** 共识别 **17 个组件**：
|
||||
|
||||
| # | 组件ID | 类型 | 结果 |
|
||||
|---|--------|------|------|
|
||||
| 1-6 | 1217501-1217506 | 对话朗读 | ✅ |
|
||||
| 7-8 | 1217507-1217508 | 对话挖空 | ✅ |
|
||||
| 9 | 1217509 | 对话选择 | ✅ |
|
||||
| 10 | 1217510 | 对话组句 | ✅ |
|
||||
| 11-12 | 1217511-1217512 | 对话挖空 | ✅ |
|
||||
| 13 | 1217513 | 对话选读 | ✅ |
|
||||
| 14 | 1217514 | 对话挖空 | ✅ |
|
||||
| 15 | 1217515 | 对话组句 | ✅ |
|
||||
| 16 | 1217516 | 对话选读 | ✅ |
|
||||
| 17 | 1217517 | 口语独白 | ⏭️ 跳过 |
|
||||
|
||||
✅ 成功 16 / 跳过 1（口语独白尚未实现生成器）/ 失败 0，HTML 报告已发送。
|
||||
user: https://makee-interactive.feishu.cn/wiki/P9bvw6nXziqzWZkxDmMcOZN4ndc 组件配置-json
|
||||
assistant: 处理完成。**L1-S2-U18-L5 双龙会** 共识别 **16 个组件**，全部成功：
|
||||
|
||||
| # | 组件ID | 类型 | cType |
|
||||
|---|--------|------|-------|
|
||||
| 1-3 | 1218501-1218503 | 对话朗读 | mid_dialog_repeat |
|
||||
| 4 | 1218504 | 对话挖空 | mid_dialog_fillin |
|
||||
| 5 | 1218505 | 对话朗读 | mid_dialog_repeat |
|
||||
| 6 | 1218506 | 对话挖空 | mid_dialog_fillin |
|
||||
| 7 | 1218507 | 对话选读 | mid_dialog_select |
|
||||
| 8 | 1218508 | 看图拼词 | core_writing_imgMakeWord |
|
||||
| 9 | 1218509 | 对话选择 | mid_dialog_choose |
|
||||
| 10 | 1218510 | 对话朗读 | mid_dialog_repeat |
|
||||
| 11 | 1218511 | 图片单选 | mid_image_choose |
|
||||
| 12 | 1218512 | 对话组句 | mid_dialog_sentence |
|
||||
| 13 | 1218513 | 对话表达 | mid_dialog_express |
|
||||
| 14 | 1218514 | 对话组句 | mid_dialog_sentence |
|
||||
| 15 | 1218515 | 对话选读 | mid_dialog_select |
|
||||
|
||||
✅ 成功 16 / 失败 0 / 跳过 0，HTML 报告已发送。
|
||||
41
memory/2026-06-09-1447.md
Normal file
41
memory/2026-06-09-1447.md
Normal file
@ -0,0 +1,41 @@
|
||||
# Session: 2026-06-09 14:47:42 GMT+8
|
||||
|
||||
- **Session Key**: agent:xiaoyan:feishu:direct:ou_8b79b7f711e42a4004f3f3c225a1a99c
|
||||
- **Session ID**: b3e1dcfe-a4af-4a5b-9fee-ea59d4b76826
|
||||
- **Source**: feishu
|
||||
|
||||
## Conversation Summary
|
||||
|
||||
user: https://makee-interactive.feishu.cn/wiki/O7RwwMei6iQCFlk6cHocDqGpn9b 组件配置
|
||||
assistant: 张昆鹏，开始处理组件配置。
|
||||
|
||||
|
||||
assistant: 📝 组件配置完成，**16/16 全部成功**。
|
||||
|
||||
| 组件类型 | 数量 |
|
||||
|----------|------|
|
||||
| 对话朗读 (mid_dialog_repeat) | 6 |
|
||||
| 图片单选 (mid_image_choose) | 3 |
|
||||
| 对话挖空 (mid_dialog_fillin) | 3 |
|
||||
| 对话选读 (mid_dialog_select) | 1 |
|
||||
| 看图拼词 (core_writing_imgMakeWord) | 1 |
|
||||
| 图片拖拽 (mid_image_drag) | 1 |
|
||||
| 对话表达 (mid_dialog_express) | 1 |
|
||||
|
||||
HTML 报告已通过飞书发送给你，可在浏览器中打开后使用「一键推送全部配置+知识点」按钮推送到远程数据库。
|
||||
|
||||
⚠️ 注意：本轮 16 个组件的知识点匹配全部为空（词汇如 ship/boat/cross/helicopter 在 kpId 库中未匹配到），推送前建议在 HTML 中手动补充 kpId。
|
||||
user: https://makee-interactive.feishu.cn/wiki/O7RwwMei6iQCFlk6cHocDqGpn9b 组件配置
|
||||
assistant: **16/16 全部成功**，HTML 报告已发送。
|
||||
|
||||
| 组件类型 | 数量 |
|
||||
|----------|------|
|
||||
| 对话朗读 (mid_dialog_repeat) | 6 |
|
||||
| 图片单选 (mid_image_choose) | 3 |
|
||||
| 对话挖空 (mid_dialog_fillin) | 3 |
|
||||
| 对话选读 (mid_dialog_select) | 1 |
|
||||
| 看图拼词 (core_writing_imgMakeWord) | 1 |
|
||||
| 图片拖拽 (mid_image_drag) | 1 |
|
||||
| 对话表达 (mid_dialog_express) | 1 |
|
||||
|
||||
知识点匹配仍全部为空（ship/boat/cross/helicopter 等词汇在 kpId 库中未命中），推送前建议在 HTML 中手动补充。
|
||||
417
only_in_new.txt
Normal file
417
only_in_new.txt
Normal file
@ -0,0 +1,417 @@
|
||||
L2新版中存在但L2知识库中不存在的单词: 413 个
|
||||
============================================================
|
||||
序号 单词 词性 词义
|
||||
--------------------------------------------------------------------------------
|
||||
1 absolutely adv 绝对地
|
||||
2 accommodation n 住处
|
||||
3 activity n. 活动
|
||||
4 ad n. 广告
|
||||
5 advert n. 广告
|
||||
6 advice n. 建议
|
||||
7 all kinds of phrase. 各种各样的
|
||||
8 amazed adj. 惊讶的
|
||||
9 antique adj 古老的
|
||||
10 apartment n. 公寓套房
|
||||
11 apartment building n. 公寓楼
|
||||
12 app n. 应用程序
|
||||
13 babysit v 临时照看婴儿
|
||||
14 babysitter n 保姆
|
||||
15 backpacker n 背包客
|
||||
16 band n. 圈，箍，带
|
||||
17 baseball n. 棒球
|
||||
18 basketball n. 篮球
|
||||
19 bath n. 洗澡
|
||||
20 bathing suit n. 泳衣
|
||||
21 bear v. 忍受
|
||||
22 beard n. (下巴上的) 胡子
|
||||
23 because conj. 因为
|
||||
24 because of phrase. 因为
|
||||
25 beetle n 甲虫
|
||||
26 belong v. 属于
|
||||
27 below prep. 在……下面
|
||||
28 below adv. 在下面
|
||||
29 benefit n 益处
|
||||
30 beyond prep 超出
|
||||
31 blanket n. 毯子
|
||||
32 blog n. 博客
|
||||
33 blond adj. 金发的
|
||||
34 board v. 上 (船、火车、飞机等)
|
||||
35 boat v. 划船
|
||||
36 bomb n 炸弹
|
||||
37 bookcase n. 书架
|
||||
38 bookshop n. 书店
|
||||
39 bookstore n. 书店
|
||||
40 bored adj. 无聊的
|
||||
41 boss n. 老板
|
||||
42 bottle n. 瓶子
|
||||
43 brake n 刹车
|
||||
44 break down phrase. （车辆或机器）出故障
|
||||
45 bring v. 带来
|
||||
46 build v. 建造
|
||||
47 building n. 建筑物
|
||||
48 busy person n. 忙碌的人
|
||||
49 buy v. 购买
|
||||
50 by accident phrase. 偶然地
|
||||
51 by post prep phr 通过邮寄方式
|
||||
52 by the way phrase. 顺便说一下
|
||||
53 cab n 出租车
|
||||
54 cabin n 小屋
|
||||
55 cable n 电缆
|
||||
56 camel n. 骆驼
|
||||
57 camera n. 摄影机
|
||||
58 campsite n. 营地
|
||||
59 candy n. 糖果
|
||||
60 cardboard n 硬纸板
|
||||
61 career n. 职业
|
||||
62 careful adj. 小心的
|
||||
63 CD player abbr. 激光唱机
|
||||
64 cell phone n. 手机
|
||||
65 centre n. 中心
|
||||
66 charge v 充电
|
||||
67 chatroom n. 聊天室
|
||||
68 cheek n. 脸颊
|
||||
69 cinema n. 电影院
|
||||
70 circle n. 圆
|
||||
71 circus n. 马戏团
|
||||
72 classmate n. 同学
|
||||
73 classroom n. 教室
|
||||
74 clever adj. 聪明的
|
||||
75 climbing n. 攀登
|
||||
76 close to phrase. 靠近
|
||||
77 closed adj. 关闭的
|
||||
78 closet n. 衣柜
|
||||
79 clothes n. 衣服
|
||||
80 clown n. 小丑
|
||||
81 coffee n. 咖啡
|
||||
82 come back phrase. 重返
|
||||
83 comedy n. 喜剧
|
||||
84 comment n 评论
|
||||
85 congratulations n. 祝贺
|
||||
86 court n 法院；法庭
|
||||
87 crash v 碰撞
|
||||
88 creature n 生物
|
||||
89 credit card n. 信用卡
|
||||
90 crocodile n 鳄鱼
|
||||
91 crossing n. 十字路口
|
||||
92 cupboard n. 橱柜
|
||||
93 cure n 治愈方法
|
||||
94 cycle v. 骑自行车
|
||||
95 cycle n. 周期
|
||||
96 damage v 损坏
|
||||
97 damage n 损坏
|
||||
98 damaged adj 损坏的
|
||||
99 dancing n. 跳舞
|
||||
100 data n 数据
|
||||
101 decrease n 减少
|
||||
102 defeat v 击败
|
||||
103 demand n 要求
|
||||
104 dentist n. 牙医
|
||||
105 difficult adj. 困难的
|
||||
106 dirty adj. 脏的
|
||||
107 display v 显示
|
||||
108 diving n. 潜水
|
||||
109 doctor n. 医生
|
||||
110 double room phrase. 双人间
|
||||
111 dozen det 十二
|
||||
112 draw v. 抽签
|
||||
113 draw n. 抽签
|
||||
114 dream n. 梦想
|
||||
115 dream v. 梦想
|
||||
116 driving licence n. 驾驶执照
|
||||
117 drugstore n. 药店
|
||||
118 dry adj. 干燥的
|
||||
119 dry v. 变干
|
||||
120 duty-free adj 免税的
|
||||
121 DVD player n. DVD播放机
|
||||
122 each other phrase. 彼此
|
||||
123 eastern adj 东方的
|
||||
124 easy adj. 容易的
|
||||
125 easygoing adj 随和的
|
||||
126 economics n 经济学
|
||||
127 edge n 边
|
||||
128 elevator n. 电梯
|
||||
129 exam abbr. 考试
|
||||
130 exchange v 交换
|
||||
131 excited adj. 兴奋的
|
||||
132 exciting adj. 令人兴奋的
|
||||
133 extinct adj. 不再存在的；已灭绝的
|
||||
134 face vt. 面对
|
||||
135 face to face adv 面对面地
|
||||
136 face-to-face adj 面对面的
|
||||
137 fall over phr v 摔倒；跌倒
|
||||
138 famous adj. 著名的
|
||||
139 fancy v 想要
|
||||
140 farm v. 耕种
|
||||
141 favourite adj. 最喜欢的
|
||||
142 fax n 传真机
|
||||
143 feel v. 感觉
|
||||
144 female adj 女性的
|
||||
145 field n. 领域
|
||||
146 film n. 电影
|
||||
147 fire n. 火
|
||||
148 fish n. 鱼
|
||||
149 flat adj. 平坦的
|
||||
150 floor n. 地板
|
||||
151 foot n. 英尺
|
||||
152 football n. 橄榄球
|
||||
153 forest n. 森林
|
||||
154 French fries n. 炸薯条
|
||||
155 frightened adj. 害怕的
|
||||
156 frog n. 青蛙
|
||||
157 fruit n. 果实；结果
|
||||
158 gallery n. 画廊
|
||||
159 gap n 缺口
|
||||
160 garden v. 从事园艺活动
|
||||
161 gas station n. 加油站
|
||||
162 generally adv 通常
|
||||
163 generation n 一代
|
||||
164 get fit phrase. 健身
|
||||
165 get married phrase. 结婚
|
||||
166 give somebody a call/ring phrase. 给某人打电话
|
||||
167 glass n. 玻璃
|
||||
168 go out phrase. 出去
|
||||
169 goat n. 山羊
|
||||
170 grass n. 草地
|
||||
171 grill n 烤架
|
||||
172 grocery store n. 杂货店
|
||||
173 grow up phrase. 长大
|
||||
174 guest-house n. 招待所
|
||||
175 gym n. 体育馆
|
||||
176 haircut n 理发
|
||||
177 hairdresser n 理发师
|
||||
178 hairdryer n 吹风机
|
||||
179 hall n. 大厅
|
||||
180 hand vt. 传递，交递
|
||||
181 head v. 带领
|
||||
182 headphones n. 耳机
|
||||
183 helpful adj. 有帮助的
|
||||
184 hip hop n. 嘻哈音乐
|
||||
185 hire n 被雇佣的人
|
||||
186 hit n. 击打
|
||||
187 hobby n. 爱好
|
||||
188 hockey n. 曲棍球, 冰上曲棍球
|
||||
189 holiday n. 假期
|
||||
190 hoodie n 带兜帽的厚运动衫
|
||||
191 horror n. 恐怖
|
||||
192 hospital n. 医院
|
||||
193 hot adj. 热的
|
||||
194 hour n. 小时
|
||||
195 house n. 房子
|
||||
196 huge adj. 巨大的
|
||||
197 hungry adj. 饥饿的
|
||||
198 ice hockey n 冰球
|
||||
199 ice skates n 冰鞋
|
||||
200 ice skating n 滑冰
|
||||
201 ID/ID card abbr. 身份证
|
||||
202 idea n. 想法
|
||||
203 in front of phrase. 在……前面
|
||||
204 indoors adv. 在室内
|
||||
205 instead of prep. 代替
|
||||
206 interesting adj. 有趣的
|
||||
207 internet n. 互联网
|
||||
208 interview n 面试
|
||||
209 invitation n. 邀请
|
||||
210 invite v. 邀请
|
||||
211 iron n 铁
|
||||
212 jacket n. 夹克衫
|
||||
213 jail n 监狱
|
||||
214 jar n 罐子
|
||||
215 jealous adj 嫉妒的
|
||||
216 jeans n. 牛仔裤
|
||||
217 jet n 喷气式飞机
|
||||
218 job n. 工作
|
||||
219 kick v. 踢
|
||||
220 knife n. 刀
|
||||
221 laptop n. 笔记本电脑
|
||||
222 late adj. 迟的
|
||||
223 length n. 长度
|
||||
224 lesson n. 经验教训
|
||||
225 letter n. 信函
|
||||
226 lie down phrase. 躺下
|
||||
227 life n. 生命
|
||||
228 limit n 限制
|
||||
229 live adv. 现场直播地
|
||||
230 live adj 现场直播的
|
||||
231 lizard n 蜥蜴
|
||||
232 load n 负载
|
||||
233 lorry n. 卡车
|
||||
234 lose v. 失去
|
||||
235 lots n. 许多
|
||||
236 luckily adv. 幸运地
|
||||
237 make sure phrase. 确保
|
||||
238 male adj 男性的
|
||||
239 many pron. 许多(人或物)
|
||||
240 market n. 市场
|
||||
241 maths abbr. 数学
|
||||
242 maximum n 最大值
|
||||
243 meet v. 遇见
|
||||
244 microphone n 麦克风
|
||||
245 minimum n 最小值
|
||||
246 mirror n. 镜子
|
||||
247 mobile phone n. 手机
|
||||
248 monthly adj. 每月的
|
||||
249 mountain n. 山
|
||||
250 movie n. 电影
|
||||
251 movie star n. 电影明星
|
||||
252 movie theater phrase. 电影院
|
||||
253 musician n. 音乐家
|
||||
254 name v. 命名；给…取名
|
||||
255 nationality n. 国籍
|
||||
256 natural adj. 自然的
|
||||
257 near adj. 近的
|
||||
258 nearby adj 附近的
|
||||
259 nest n 鸟巢；鸟窝
|
||||
260 nice adj. 友好的，亲切的，讨人喜欢的
|
||||
261 niece n 侄女
|
||||
262 noise n. 噪音
|
||||
263 northeast n 东北部
|
||||
264 northwest n 西北
|
||||
265 number n. 数字
|
||||
266 nurse n. 护士
|
||||
267 o'clock n. 点钟
|
||||
268 octopus n 章鱼
|
||||
269 often adv. 经常
|
||||
270 outdoor adj. 户外的
|
||||
271 outdoors adv. 在户外
|
||||
272 outdoors n. 户外
|
||||
273 overnight adj 一整夜的
|
||||
274 paint n. 油漆
|
||||
275 paint v. 在…刷油漆；
|
||||
276 painting n. 刷油漆
|
||||
277 parent n. 父亲（或母亲）
|
||||
278 parking lot n. 停车场
|
||||
279 party n. （同行的）群，组，队
|
||||
280 pencil case phrase. 铅笔盒
|
||||
281 penny n. 便士
|
||||
282 people n. 人
|
||||
283 pharmacy n. 药店
|
||||
284 phone v. 打电话给
|
||||
285 photo n. 照片
|
||||
286 piano n. 钢琴
|
||||
287 pick up phrase. （开车）接载（人）
|
||||
288 picnic v. 去野餐
|
||||
289 picture n. 图片
|
||||
290 pin n 别针
|
||||
291 pink n. 粉色
|
||||
292 plane n. 飞机
|
||||
293 plant n. 植物
|
||||
294 plant v. 种植
|
||||
295 plug n 插头
|
||||
296 police n. 警察
|
||||
297 police car n. 警车
|
||||
298 poster n. 海报
|
||||
299 practise v. 练习
|
||||
300 present adj. 现在的
|
||||
301 pretty adv. 相当
|
||||
302 problem n. 问题
|
||||
303 professional adj 专业的
|
||||
304 put on phrase. 穿上
|
||||
305 question v. 询问
|
||||
306 quickly adv. 迅速地
|
||||
307 quiet adj. 安静的
|
||||
308 radio n. 收音机
|
||||
309 reading v. 阅读
|
||||
310 recently adv 不久前；最近
|
||||
311 refrigerator n. 冰箱
|
||||
312 request v 请求
|
||||
313 rescue v 营救
|
||||
314 reserve v 预订
|
||||
315 review n. 评论
|
||||
316 right hand n. 右手
|
||||
317 roof n. 屋顶
|
||||
318 ruler n. 统治者；支配者
|
||||
319 run v. 管理，经营
|
||||
320 safe adj. 安全的
|
||||
321 sail v. 航行
|
||||
322 sauce n. 调味汁
|
||||
323 sausage n. 香肠
|
||||
324 scarf n. 围巾
|
||||
325 scary adj. 可怕的
|
||||
326 science n. 科学
|
||||
327 second-hand adv 二手地
|
||||
328 sell v. 卖
|
||||
329 signal n 信号
|
||||
330 sit down phrase. 坐下
|
||||
331 skate v. 滑冰
|
||||
332 skateboard n. 滑板
|
||||
333 skateboarding n. 滑板运动
|
||||
334 sleep n. 睡眠
|
||||
335 sleep v. 睡觉
|
||||
336 slow adj. 缓慢的
|
||||
337 slow v. 放慢
|
||||
338 slowly adv. 慢慢地
|
||||
339 check in phr. v. 托运
|
||||
340 snowboard v. 滑雪板运动
|
||||
341 soccer n. 足球
|
||||
342 southwest n 西南部
|
||||
343 spider n 蜘蛛
|
||||
344 sport n. 运动
|
||||
345 sports centre n. 体育中心
|
||||
346 square n. 广场
|
||||
347 stairs n. 楼梯
|
||||
348 stand v. 位于
|
||||
349 stand v. 忍受，承受
|
||||
350 stomach n. 胃
|
||||
351 stomachache n. 胃痛
|
||||
352 store n. 商店
|
||||
353 store v. 储存
|
||||
354 street n. 街道
|
||||
355 strike n 罢工
|
||||
356 striped adj. 有条纹的
|
||||
357 stripes n. 条纹
|
||||
358 study n. 学习
|
||||
359 sugar n. 糖
|
||||
360 supermarket n. 超市
|
||||
361 support v 支持
|
||||
362 surfboard n. 冲浪板
|
||||
363 surprising adj. 令人惊讶的
|
||||
364 swan n. 天鹅
|
||||
365 switch n 开关
|
||||
366 tablet n. 药片
|
||||
367 tail n. 尾巴
|
||||
368 take off phrase. （飞机等）起飞
|
||||
369 tear n 眼泪
|
||||
370 television n. 电视机
|
||||
371 terrible adj. 可怕的
|
||||
372 thirsty adj. 渴望的，渴求的
|
||||
373 those pron. 那些
|
||||
374 throw v. 扔
|
||||
375 ticket n. 票
|
||||
376 tidy v. 整理
|
||||
377 tights n. 紧身裤
|
||||
378 topic n. 话题
|
||||
379 tortoise n. 陆龟；海龟
|
||||
380 tourist information centre phrase. 游客信息中心
|
||||
381 train v. 训练
|
||||
382 transport n. 运输
|
||||
383 travel n. 旅行
|
||||
384 try on phrase. 试穿
|
||||
385 umbrella n. 雨伞
|
||||
386 update v 更新
|
||||
387 vegetable n. 蔬菜
|
||||
388 vegetarian n 素食者
|
||||
389 video adj. 视频的
|
||||
390 video n 视频
|
||||
391 village n. 村庄
|
||||
392 visiting adj. 访问的
|
||||
393 wake v. 醒来
|
||||
394 wake up phrase. 叫醒
|
||||
395 walk v. 牵着（动物）走
|
||||
396 walking n. 步行
|
||||
397 wall n. 墙
|
||||
398 wardrobe n. 衣柜
|
||||
399 wash v 洗
|
||||
400 wash up phrase. 洗漱
|
||||
401 well known adj. 众所周知的
|
||||
402 whale v. 捕鲸
|
||||
403 white n. 白色
|
||||
404 wife n. 妻子
|
||||
405 wildlife n. 野生动物
|
||||
406 wind v. 蜿蜒前进，迂回
|
||||
407 windsurfing n. 帆板运动
|
||||
408 worst adj. 最差的
|
||||
409 worst adv. 最糟糕地
|
||||
410 writer n. 作家
|
||||
411 writing n. 写作
|
||||
412 yellow n. 黄色
|
||||
413 yoghurt n. 酸奶
|
||||
@ -1 +0,0 @@
|
||||
userId,userMsg,source,userName,soeData,audioUrl,asrStatus,componentId,componentType,dataVersion,timeStr
|
||||
|
BIN
output/KET单词表_2025.xlsx
Normal file
BIN
output/KET单词表_2025.xlsx
Normal file
Binary file not shown.
BIN
output/L1单词表_980词.xlsx
Normal file
BIN
output/L1单词表_980词.xlsx
Normal file
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
81
regenerate_files.py
Normal file
81
regenerate_files.py
Normal file
@ -0,0 +1,81 @@
|
||||
|
||||
import pandas as pd
|
||||
import os
|
||||
|
||||
# ---------------------------
|
||||
# 重新生成并保存到正确位置
|
||||
# ---------------------------
|
||||
|
||||
# 文件路径
|
||||
file3034 = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/30-34.xlsx'
|
||||
fileLevel3 = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/三级单词表原始版.xlsx'
|
||||
fileKet = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/KET词汇表_完整版.xlsx'
|
||||
|
||||
outputDir = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表'
|
||||
|
||||
# 1. 生成【是否三级】版本
|
||||
df3034 = pd.read_excel(file3034)
|
||||
dfLevel3 = pd.read_excel(fileLevel3)
|
||||
|
||||
# 创建三级词汇表字典
|
||||
level3_dict = {}
|
||||
for idx, row in dfLevel3.iterrows():
|
||||
word = str(row['单词']).strip().lower()
|
||||
pos = row['三级词性'] if pd.notna(row['三级词性']) else row['A2词性']
|
||||
meaning = row['三级中文释义'] if pd.notna(row['三级中文释义']) else row['A2中文释义']
|
||||
level3_dict[word] = {'row': idx+2, 'pos': pos, 'meaning': meaning}
|
||||
|
||||
# 添加【是否三级】列
|
||||
result_level3 = []
|
||||
for idx, row in df3034.iterrows():
|
||||
word = str(row['单词']).strip().lower()
|
||||
if word in level3_dict:
|
||||
info = level3_dict[word]
|
||||
result_level3.append(f"【是】- 第{info['row']}行 - {info['pos']} - {info['meaning']}")
|
||||
else:
|
||||
result_level3.append("【否】")
|
||||
df3034['是否三级'] = result_level3
|
||||
|
||||
# 保存文件1
|
||||
output1 = os.path.join(outputDir, '30-34_三级标注完成.xlsx')
|
||||
df3034.to_excel(output1, index=False)
|
||||
print(f"已保存: {output1}")
|
||||
|
||||
# 2. 生成【是否KET】版本
|
||||
df3034_ket = pd.read_excel(file3034)
|
||||
dfKet = pd.read_excel(fileKet)
|
||||
|
||||
# 创建KET词汇表字典
|
||||
ket_dict = {}
|
||||
for idx, row in dfKet.iterrows():
|
||||
word = str(row['单词']).strip().lower()
|
||||
ket_dict[word] = {'row': idx+2, 'pos': row['词性']}
|
||||
|
||||
# 添加【是否KET】列
|
||||
result_ket = []
|
||||
for idx, row in df3034_ket.iterrows():
|
||||
word = str(row['单词']).strip().lower()
|
||||
if word in ket_dict:
|
||||
info = ket_dict[word]
|
||||
result_ket.append(f"【是】- 第{info['row']}行 - {info['pos']}")
|
||||
else:
|
||||
result_ket.append("【否】")
|
||||
df3034_ket['是否KET'] = result_ket
|
||||
|
||||
# 保存文件2
|
||||
output2 = os.path.join(outputDir, '30-34_KET标注完成.xlsx')
|
||||
df3034_ket.to_excel(output2, index=False)
|
||||
print(f"已保存: {output2}")
|
||||
|
||||
# 3. 生成同时有两个标注的版本
|
||||
df3034_both = pd.read_excel(file3034)
|
||||
df3034_both['是否三级'] = result_level3
|
||||
df3034_both['是否KET'] = result_ket
|
||||
output3 = os.path.join(outputDir, '30-34_完整标注版.xlsx')
|
||||
df3034_both.to_excel(output3, index=False)
|
||||
print(f"已保存: {output3}")
|
||||
|
||||
print("\n所有文件已保存到: L2单词表目录")
|
||||
print("\n目录内容:")
|
||||
for f in os.listdir(outputDir):
|
||||
print(f" - {f}")
|
||||
73
verify_ket.py
Normal file
73
verify_ket.py
Normal file
@ -0,0 +1,73 @@
|
||||
|
||||
import pandas as pd
|
||||
|
||||
# 读取结果文件
|
||||
fileResult = r'/root/.openclaw/workspace-xiaoyan/output/30-34_KET标注完成.xlsx'
|
||||
fileKet = r'/root/.openclaw/workspace-xiaoyan/business_knowledge/L2单词表/KET词汇表_完整版.xlsx'
|
||||
|
||||
dfResult = pd.read_excel(fileResult)
|
||||
dfKet = pd.read_excel(fileKet)
|
||||
|
||||
print("=== 复核检查 ===")
|
||||
print(f"结果文件总行数: {len(dfResult)}")
|
||||
print(f"结果文件列名: {dfResult.columns.tolist()}")
|
||||
|
||||
# 创建KET词汇表的快速查找字典
|
||||
ket_words = set()
|
||||
for idx, row in dfKet.iterrows():
|
||||
word = str(row['单词']).strip().lower()
|
||||
ket_words.add(word)
|
||||
|
||||
# 手动复核前20个和后20个单词
|
||||
print("\n=== 手动复核前20个单词 ===")
|
||||
correct = 0
|
||||
wrong = 0
|
||||
for idx in range(min(20, len(dfResult))):
|
||||
row = dfResult.iloc[idx]
|
||||
word = str(row['单词']).strip().lower()
|
||||
is_ket = word in ket_words
|
||||
marked_as_ket = '【是】' in str(row['是否KET'])
|
||||
|
||||
status = "✓" if is_ket == marked_as_ket else "✗"
|
||||
if is_ket == marked_as_ket:
|
||||
correct += 1
|
||||
else:
|
||||
wrong += 1
|
||||
|
||||
print(f"{idx+1:2d}. {row['单词']:20s} 标注: {'是' if marked_as_ket else '否':3s} 实际: {'是' if is_ket else '否':3s} {status}")
|
||||
|
||||
print(f"\n前20个 - 正确: {correct}, 错误: {wrong}")
|
||||
|
||||
# 复核后20个
|
||||
print("\n=== 手动复核后20个单词 ===")
|
||||
correct2 = 0
|
||||
wrong2 = 0
|
||||
for idx in range(max(0, len(dfResult)-20), len(dfResult)):
|
||||
row = dfResult.iloc[idx]
|
||||
word = str(row['单词']).strip().lower()
|
||||
is_ket = word in ket_words
|
||||
marked_as_ket = '【是】' in str(row['是否KET'])
|
||||
|
||||
status = "✓" if is_ket == marked_as_ket else "✗"
|
||||
if is_ket == marked_as_ket:
|
||||
correct2 += 1
|
||||
else:
|
||||
wrong2 += 1
|
||||
|
||||
print(f"{idx+1:3d}. {row['单词']:20s} 标注: {'是' if marked_as_ket else '否':3s} 实际: {'是' if is_ket else '否':3s} {status}")
|
||||
|
||||
print(f"\n后20个 - 正确: {correct2}, 错误: {wrong2}")
|
||||
|
||||
# 复核所有标注为【否】的单词
|
||||
print("\n=== 复核标注为【否】的所有单词 ===")
|
||||
no_words = dfResult[dfResult['是否KET'] == '【否】']
|
||||
print(f"标注为【否】的单词共 {len(no_words)} 个:")
|
||||
all_correct = True
|
||||
for idx, row in no_words.iterrows():
|
||||
word = str(row['单词']).strip().lower()
|
||||
if word in ket_words:
|
||||
print(f"✗ {row['单词']} 应该是【是】，但标注为【否】")
|
||||
all_correct = False
|
||||
|
||||
if all_correct:
|
||||
print("✓ 所有标注为【否】的单词都是正确的！")
|
||||
Loading…
Reference in New Issue
Block a user