143 lines
4.8 KiB
Python
143 lines
4.8 KiB
Python
import psycopg2
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conn = psycopg2.connect(
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host="192.168.0.61",
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port=55432,
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database="njts_acct",
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user="njts_app",
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password="njts_app2025"
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)
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cur = conn.cursor()
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print("=" * 140)
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print("【识别可能的6月→7月/8月复制数据】")
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print("=" * 140)
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# 第1步：找出6月所有有的仕訳（描述+金额的组合）
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print("\n第1步：提取6月的所有仕訳（描述和金额作为KEY）")
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cur.execute("""
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SELECT DISTINCT
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je.description,
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COALESCE(SUM(jl.debit), 0)::BIGINT as total_debit,
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COALESCE(SUM(jl.credit), 0)::BIGINT as total_credit
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FROM journal_entries je
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LEFT JOIN journal_lines jl ON je.journal_entry_id = jl.journal_entry_id
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WHERE je.is_deleted = false
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AND je.is_latest = true
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AND EXTRACT(YEAR FROM je.entry_date) = 2025
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AND EXTRACT(MONTH FROM je.entry_date) = 6
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GROUP BY je.journal_entry_id, je.description
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ORDER BY je.description
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""")
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june_patterns = cur.fetchall()
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print(f"找到 {len(june_patterns)} 个6月的仕訳模式")
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# 第2步：在7月和8月中查找相同的仕訳
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print("\n第2步：在7月和8月中查找相同描述和金额的仕訳")
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duplicates_for_deletion = []
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for description, debit, credit in june_patterns:
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cur.execute("""
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SELECT
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je.journal_entry_id,
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TO_CHAR(je.entry_date, 'YYYY-MM-DD') as entry_date,
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EXTRACT(MONTH FROM je.entry_date)::INT as month,
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COALESCE(SUM(jl.debit), 0)::BIGINT as calc_debit,
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COALESCE(SUM(jl.credit), 0)::BIGINT as calc_credit
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FROM journal_entries je
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LEFT JOIN journal_lines jl ON je.journal_entry_id = jl.journal_entry_id
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WHERE je.is_deleted = false
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AND je.is_latest = true
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AND EXTRACT(YEAR FROM je.entry_date) = 2025
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AND EXTRACT(MONTH FROM je.entry_date) IN (7, 8)
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AND je.description = %s
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GROUP BY je.journal_entry_id, je.entry_date
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HAVING COALESCE(SUM(jl.debit), 0) = %s
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AND COALESCE(SUM(jl.credit), 0) = %s
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""", (description, debit, credit))
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matches = cur.fetchall()
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if matches:
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for entry_id, entry_date, month, calc_debit, calc_credit in matches:
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duplicates_for_deletion.append({
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'entry_id': entry_id,
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'date': entry_date,
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'month': month,
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'description': description,
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'debit': debit,
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'credit': credit
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})
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# 按月份排序
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duplicates_for_deletion.sort(key=lambda x: (x['month'], x['entry_id']))
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print(f"\n找到 {len(duplicates_for_deletion)} 个可能的复制记录（7月/8月中与6月相同的）")
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if duplicates_for_deletion:
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print("\n【可能的复制记录列表】\n")
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current_month = None
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for dup in duplicates_for_deletion:
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if dup['month'] != current_month:
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print(f"\n--- {dup['month']}月 ---")
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current_month = dup['month']
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print(f"ID={dup['entry_id']:3d} | {dup['date']} | 借={dup['debit']:>12,} 貸={dup['credit']:>12,}")
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print(f" └─ {dup['description'][:80]}")
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print("\n" + "=" * 140)
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print(f"【预期删除】{len(duplicates_for_deletion)} 条记录")
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print("=" * 140)
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# 计算删除前后的普通預金期末残高变化
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cur.execute("""
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SELECT
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COALESCE(SUM(COALESCE(jl.debit, 0) - COALESCE(jl.credit, 0)), 0) as balance
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FROM journal_lines jl
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INNER JOIN journal_entries je ON jl.journal_entry_id = je.journal_entry_id
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WHERE jl.account_id = 2 -- 普通預金
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AND je.is_deleted = false
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AND je.is_latest = true
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""")
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current_balance = cur.fetchone()[0]
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print(f"\n删除前普通預金期末残高: {current_balance:>15,.0f}円")
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# 计算这些重复记录对普通預金的影响
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deletion_impact = 0
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for dup in duplicates_for_deletion:
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cur.execute("""
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SELECT COALESCE(SUM(COALESCE(jl.debit, 0) - COALESCE(jl.credit, 0)), 0)
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FROM journal_lines jl
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WHERE jl.journal_entry_id = %s
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AND jl.account_id = 2 -- 普通預金
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""", (dup['entry_id'],))
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impact = cur.fetchone()[0]
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deletion_impact += impact
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expected_balance_after = current_balance - deletion_impact
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print(f"这些记录对普通預金的总影响: {deletion_impact:>15,.0f}円")
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print(f"删除后预期普通預金期末残高: {expected_balance_after:>15,.0f}円")
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print(f"\n期望目标值: 14,916,322円")
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print(f"删除后vs目标值差距: {14_916_322 - expected_balance_after:>15,.0f}円")
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cur.close()
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conn.close()
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# 保存删除列表到Python变量以供后续使用
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print("\n" + "=" * 140)
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print(f"识别完成。找到 {len(duplicates_for_deletion)} 条可能的复制记录")
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print("=" * 140)
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# 返回delete_list供后续使用
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import json
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print("\n【识别的删除对象IDs】")
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delete_ids = [dup['entry_id'] for dup in duplicates_for_deletion]
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print(delete_ids)
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