Update README and project cleanup

scripts/quality-check-1.py

@@ -0,0 +1,124 @@
+#!/usr/bin/env python3
+"""全面质量检查脚本"""
+import openpyxl
+import sqlite3
+
+print("\n" + "="*70)
+print("🔍 全面质量检查")
+print("="*70 + "\n")
+
+# ============================================================================
+# 1. Excel 文件对比
+# ============================================================================
+print("1️⃣  EXCEL 文件结构和内容对比")
+print("-"*70 + "\n")
+
+wb0 = openpyxl.load_workbook('/Users/inkling/Desktop/dmp/家庭教育档案-天数.xlsx')
+wb1 = openpyxl.load_workbook('/Users/inkling/Desktop/dmp/清洗1.0.xlsx')
+wb2 = openpyxl.load_workbook('/Users/inkling/Desktop/dmp/清洗2.0.xlsx')
+
+ws0, ws1, ws2 = wb0.active, wb1.active, wb2.active
+
+print(f"📊 行列统计:")
+print(f"  原始(家庭教育档案-天数): {ws0.max_row} rows × {ws0.max_column} cols")
+print(f"  清洗1.0:             {ws1.max_row} rows × {ws1.max_column} cols")
+print(f"  清洗2.0:             {ws2.max_row} rows × {ws2.max_column} cols")
+
+# 列结构对比
+print(f"\n📋 列结构对比:")
+print(f"  {'列':<3} {'原始':<25} {'清洗1.0':<25} {'清洗2.0':<25} {'状态':<5}")
+print(f"  {'-'*3} {'-'*25} {'-'*25} {'-'*25} {'-'*5}")
+
+for col in range(1, 17):
+    h0 = str(ws0.cell(1, col).value or '')[:22]
+    h1 = str(ws1.cell(1, col).value or '')[:22]
+    h2 = str(ws2.cell(1, col).value or '')[:22]
+    match = "✓" if h1 == h2 else "✗"
+    print(f"  {col:<3} {h0:<25} {h1:<25} {h2:<25} {match:<5}")
+
+# 数据完整性检查
+print(f"\n✅ 数据完整性 (前100行检查):")
+
+def check_null_rate(ws, start_col=1, end_col=16, rows=100):
+    results = {}
+    for col in range(start_col, min(end_col + 1, ws.max_column + 1)):
+        nulls = 0
+        total = 0
+        for row in range(2, min(rows + 2, ws.max_row + 1)):
+            total += 1
+            if ws.cell(row, col).value is None:
+                nulls += 1
+        if total > 0:
+            results[col] = (nulls, total, 100 * nulls / total)
+    return results
+
+nulls1 = check_null_rate(ws1)
+nulls2 = check_null_rate(ws2)
+
+print(f"  清洗1.0: ", end="")
+if all(rate == 0 for _, _, rate in nulls1.values()):
+    print("✓ 完全无缺失值")
+else:
+    for col, (n, t, rate) in sorted(nulls1.items()):
+        if rate > 0:
+            print(f"列{col}({rate:.0f}%) ", end="")
+
+print(f"\n  清洗2.0: ", end="")
+if all(rate == 0 for _, _, rate in nulls2.values()):
+    print("✓ 完全无缺失值")
+else:
+    for col, (n, t, rate) in sorted(nulls2.items()):
+        if rate > 0:
+            print(f"列{col}({rate:.0f}%) ", end="")
+print()
+
+# ============================================================================
+# 2. 数据库内容检查
+# ============================================================================
+print(f"\n\n2️⃣  数据库内容检查")
+print("-"*70 + "\n")
+
+conn = sqlite3.connect('/Users/inkling/Desktop/dmp/dmp_onion.db')
+cursor = conn.cursor()
+
+# 用户数据
+cursor.execute('SELECT COUNT(*) FROM users')
+user_count = cursor.fetchone()[0]
+print(f"👥 用户数: {user_count}")
+
+# 标签数据
+cursor.execute('SELECT COUNT(*) FROM tags')
+tag_count = cursor.fetchone()[0]
+print(f"🏷️  标签数: {tag_count}")
+
+# 分类数据
+cursor.execute('SELECT COUNT(*) FROM tag_categories')
+cat_count = cursor.fetchone()[0]
+print(f"📂 分类数: {cat_count}")
+
+# 关系数据
+cursor.execute('SELECT COUNT(*) FROM user_tags')
+rel_count = cursor.fetchone()[0]
+print(f"🔗 关系数: {rel_count}")
+
+# 分类分布
+print(f"\n📊 标签分类分布:")
+cursor.execute('''
+    SELECT tc.name, COUNT(DISTINCT t.id) as tag_count, 
+           COUNT(DISTINCT ut.user_id) as user_count,
+           COUNT(ut.id) as rel_count
+    FROM tag_categories tc
+    LEFT JOIN tags t ON tc.id = t.category_id
+    LEFT JOIN user_tags ut ON t.id = ut.tag_id
+    GROUP BY tc.id
+    ORDER BY tc.id
+''')
+
+for row in cursor.fetchall():
+    name, tags, users, rels = row
+    coverage = f"{(users*100/user_count):.0f}%" if users else "0%"
+    print(f"  • {name:<20} {tags:3d} tags, {users:4d} users ({coverage:>3s}), {rels:5d} relations")
+
+conn.close()
+
+print("\n" + "="*70)