From a268c4e74d99caa4f7ae7a3b899960d759a21e2d Mon Sep 17 00:00:00 2001
From: Hermes CI Fix <hermes-ci@sxbh.ltd>
Date: Thu, 13 Aug 2026 01:17:32 +0800
Subject: [PATCH] =?UTF-8?q?fix:=20=E6=99=BA=E8=83=BD=E5=AF=BC=E5=85=A5?=
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- kpi_code_patterns 名称类列优先(科目名称/指标名称/name)，编码类列兜底
- 修复科目余额表表头(科目编码|科目名称)检测失败fallback cols[0]导致名称存成编码
- 导入循环增加防护: NaN/空/纯数字名称跳过，不再创建垃圾KPI
- 存量数据: 14个一级科目名称已按科目表修正，82个明细标记待财务核对，EXT_071标记未命名
---
 backend/app/api/data.py | 20 ++++++++++++++------
 1 file changed, 14 insertions(+), 6 deletions(-)

diff --git a/backend/app/api/data.py b/backend/app/api/data.py
index e8efdfd7..8125eb1d 100644
--- a/backend/app/api/data.py
+++ b/backend/app/api/data.py
@@ -80,11 +80,11 @@ async def import_excel(file: UploadFile = File(...),
 # ── 智能导入（BOT自动识别，无需手动映射） ──
 
 _SMART_MAP = {
-    # KPI编码列匹配模式 → 标准kpi_code
+    # KPI名称/编码列匹配模式（顺序重要：名称类列优先，避免科目编码被当名称）
     "kpi_code_patterns": [
-        re.compile(r'^(kpi_?code|指标编码|编码)$', re.I),
-        re.compile(r'^(科目|项目|账户|报表项目|项目名称)$'),
-        re.compile(r'^(指标名称?|kpi名称?|name)$', re.I),
+        re.compile(r'^(科目名称|项目名称|指标名称?|kpi名称?|name|名称)$', re.I),
+        re.compile(r'^(科目编码|科目代码|kpi_?code|指标编码|编码)$', re.I),
+        re.compile(r'^(科目|项目|账户|报表项目)$'),
     ],
     # 期间列匹配
     "period_patterns": [
@@ -218,11 +218,19 @@ async def import_excel_smart(
     skipped_rows = []
 
     for idx, row in df.iterrows():
-        raw_kpi = str(row.get(kpi_col, "")).strip()
+        raw_kpi_raw = row.get(kpi_col, "")
+        raw_kpi = str(raw_kpi_raw).strip()
         raw_val = row.get(value_col)
         raw_period = str(row.get(period_col, period or "")).strip() if period_col else (period or "")
 
-        if not raw_kpi or pd.isna(raw_val):
+        # 名称防护：NaN/空/None 或 纯数字（疑似科目编码被误当名称）→ 跳过，避免创建垃圾KPI
+        if raw_kpi.lower() in ("nan", "none") or not raw_kpi:
+            skipped_rows.append(f"第{idx+2}行: KPI名称为空")
+            continue
+        if re.fullmatch(r"\d+(\.\d+)?", raw_kpi):
+            skipped_rows.append(f"第{idx+2}行: KPI名称疑似科目编码「{raw_kpi}」，跳过")
+            continue
+        if pd.isna(raw_val):
             skipped_rows.append(f"第{idx+2}行: 缺数据")
             continue
         if not raw_period: