diff --git a/apps/wpm/scripts/batch_gxerp.md b/apps/wpm/scripts/batch_gxerp.md
index bf6bd051..175f672c 100644
--- a/apps/wpm/scripts/batch_gxerp.md
+++ b/apps/wpm/scripts/batch_gxerp.md
@@ -241,8 +241,9 @@ FtestWork where type2 = TYPE2_SOME
 - **锚点**：批次存在锚点工段（`ZT_ANCHOR_MGROUPS=["黑化"]`）产出报工时停止，白料数 = 该批锚点工段 `mlogb_from.count_use`（进炉前领用数）。多次黑化的链取离大批最近的一次。
 - **分摊**（`_zt_source_out_ratio`）：来源批有多条**生产主线**流出边（报工改号/拆批/汇入大批格式 `ZT_BIG_BATCH_RE` 的合批）时，按各去向数量占比分摊；汇入不良集中批（如 `黑检片-00`）的旁路不占白料份额，使炉后不良计入生产主线分母。单一去向全额传递。
 - **边数量**：拆批取 `Handoverb(batch=目标批)`，合批取 `Handoverb(batch=来源批)`，报工改号取目标产出行的 `mlogb_from.count_use`；无明细时兜底整单数量。
-- **外购兜底**：无上游谱系且由入库创建 → 白料数 = 入库数量（`MIOItem.count`），口径记 `入库数量`。
-- **老数据兜底**（`_zt_white_legacy`）：BatchLog 上线前的批次无边，沿 `BatchSt.handover/mlog` 创建来源续上回溯，口径记 `谱系兜底`。
+- **外购/遗留兜底**：无上游谱系 → 白料数 = 该批原料性入库数量之和（`MIOItem`，仅 `pur_in`/`other_in`，排除生产入库），口径记 `入库数量`。
+- **老数据兜底**（`_zt_white_legacy`）：BatchLog 上线前的批次无边且无入库，沿 `BatchSt.handover/mlog` 创建来源续上回溯，口径记 `谱系兜底`。
+- **性能护栏**：回溯按批次号记忆化（网状/菱形谱系线性复杂度，防环截断过的节点不入缓存）；单次回溯节点访问超 `ZT_MAX_VISITS=3000` 时放弃，口径记 `回溯规模超限`（负缓存，不重复付代价）。
 
 ### 缓存与隔离
 
diff --git a/apps/wpm/scripts/batch_gxerp.py b/apps/wpm/scripts/batch_gxerp.py
index 9d01eb6c..f95324dd 100644
--- a/apps/wpm/scripts/batch_gxerp.py
+++ b/apps/wpm/scripts/batch_gxerp.py
@@ -22,6 +22,7 @@ myLogger = logging.getLogger("log")
 #   (汇入不良集中批的旁路不占份额), 占比截断为1; 无锚点谱系(如外购半成品)时兜底用入库数量。
 ZT_ANCHOR_MGROUPS = ["黑化"]  # 入料数锚点工段
 ZT_MAX_DEPTH = 12  # 白料回溯最大层数
+ZT_MAX_VISITS = 3000  # 单个大批白料回溯的节点访问上限(防病态网状谱系组合爆炸)
 ZT_BIG_BATCH_RE = re.compile(r"^\d{4}-[0-9A-Za-z.]+-\d+")  # 大批批号格式(合批改名产生), 用于区分生产分流与不良汇集
 
 def main(batch: str, mgroup_obj):
@@ -214,7 +215,7 @@ def handle_zt(batchst: BatchSt, data: dict):
     big_batch = resolve_zt_big(batchst, data)
     if big_batch and batchst.zt_batch != big_batch:
         batchst.zt_batch = big_batch
-        batchst.save(update_fields=["zt_batch"])
+        batchst.save(update_fields=["zt_batch", "update_time"])
     if big_batch is None and BatchSt.objects.filter(zt_batch=batchst.batch, version=1).exclude(id=batchst.id).exists():
         # 当前批自身是大批(已有子批归属于它), 其数据变化(如白料数)也需重算
         big_batch = batchst.batch
@@ -303,14 +304,28 @@ def _zt_source_out_ratio(edge: BatchLog, qty):
     return min(Decimal(qty) / total, Decimal(1))
 
 
-def _zt_white_count(batchst: BatchSt, depth=0, path=frozenset()):
+def _zt_white_count(batchst: BatchSt, depth=0, path=frozenset(), memo=None):
     """回溯计算该批对应的白料数(进炉前数量)
 
     返回 (白料数, 口径set); 不可得时白料数为 None。
-    分摊: 来源批白料 p_white 按 qty 占其全部流出量的比例分给当前批。
+    分摊: 来源批白料 p_white 按 qty 占其生产主线流出量的比例分给当前批。
+    memo 按批次号缓存本次回溯的中间结果, 网状/菱形谱系保持线性复杂度;
+    经防环截断的节点结果与路径相关, 不入缓存。visits 超限直接放弃(隔离病态谱系)。
     """
+    white, kinds, _clean = _zt_white_walk(batchst, depth, path,
+                                          memo if memo is not None else {"visits": 0, "res": {}})
+    return white, kinds
+
+
+def _zt_white_walk(batchst: BatchSt, depth, path, memo):
+    """返回 (白料数, 口径set, 结果是否未经防环截断可缓存)"""
+    if batchst.batch in memo["res"]:
+        return memo["res"][batchst.batch]
     if depth > ZT_MAX_DEPTH:
-        return None, {"超出回溯深度"}
+        return None, {"超出回溯深度"}, False
+    memo["visits"] += 1
+    if memo["visits"] > ZT_MAX_VISITS:
+        return None, {"回溯规模超限"}, False
     path = path | {batchst.batch}
 
     # 锚点: 该批有锚点工段产出报工, 白料数=锚点工段领用数
@@ -326,40 +341,47 @@ def _zt_white_count(batchst: BatchSt, depth=0, path=frozenset()):
         white += row.mlogb_from.count_use if row.mlogb_from else row.count_real
         kinds.add(f"{row.mlog.mgroup.name}领用")
     if found_anchor:
-        return white, kinds
+        memo["res"][batchst.batch] = (white, kinds, True)
+        return white, kinds, True
 
-    edges = [e for e in BatchLog.objects.filter(target=batchst).select_related("source", "handover")
-             if e.source.batch not in path]  # 跳过自环/已访问节点(同名重复改号、合回上游等)
+    all_edges = list(BatchLog.objects.filter(target=batchst).select_related("source", "handover"))
+    edges = [e for e in all_edges if e.source.batch not in path]  # 跳过自环/已访问节点(同名重复改号、合回上游等)
+    clean = len(edges) == len(all_edges)
     if not edges:
-        # 无上游谱系: 入库创建的批(如外购半成品)兜底用入库数量
-        mioitem = batchst.mioitem
-        if mioitem is None and batchst.mio_id:
-            mioitem = MIOItem.objects.filter(mio_id=batchst.mio_id, batch=batchst.batch).first()
-        if mioitem and mioitem.count:
-            return mioitem.count, {"入库数量"}
+        # 无上游谱系: 兜底汇总原料性入库数量(采购/其他入库, 排除生产入库)
+        mio_total = MIOItem.objects.filter(
+            batch=batchst.batch, mio__submit_time__isnull=False,
+            mio__type__in=["pur_in", "other_in"]).aggregate(t=Sum("count"))["t"]
+        if mio_total:
+            if clean:
+                memo["res"][batchst.batch] = (mio_total, {"入库数量"}, True)
+            return mio_total, {"入库数量"}, clean
         # BatchLog 上线前的老批次没有边, 但 BatchSt 记录了创建它的交接/报工, 由此续上回溯
-        legacy = _zt_white_legacy(batchst, depth, path)
+        legacy = _zt_white_legacy(batchst, depth, path, memo)
         if legacy is not None:
             return legacy
-        return None, {f"{batchst.batch}无上游谱系且无入库数量"}
+        return None, {f"{batchst.batch}无上游谱系且无入库数量"}, False
 
     white_total = Decimal(0)
     for edge in edges:
         qty = _zt_edge_qty(edge, batchst.batch)
         if not qty:
-            return None, {f"{edge.source.batch}->{batchst.batch}边数量缺失"}
-        p_white, p_kinds = _zt_white_count(edge.source, depth + 1, path)
+            return None, {f"{edge.source.batch}->{batchst.batch}边数量缺失"}, False
+        p_white, p_kinds, p_clean = _zt_white_walk(edge.source, depth + 1, path, memo)
         if p_white is None:
-            return None, p_kinds
+            return None, p_kinds, False
+        clean = clean and p_clean
         white_total += Decimal(p_white) * _zt_source_out_ratio(edge, qty)
         kinds |= p_kinds
-    return white_total, kinds
+    if clean:
+        memo["res"][batchst.batch] = (white_total, kinds, True)
+    return white_total, kinds, clean
 
 
-def _zt_white_legacy(batchst: BatchSt, depth, path):
+def _zt_white_legacy(batchst: BatchSt, depth, path, memo):
     """BatchLog 上线前的老批次没有拆合批边, 根据 BatchSt 记录的创建来源续上回溯
 
-    可解时返回 (白料数, 口径set), 否则返回 None。
+    可解时返回 (白料数, 口径set, 可缓存标记), 否则返回 None。
     """
     src_batch = None
     qty = None
@@ -385,10 +407,10 @@ def _zt_white_legacy(batchst: BatchSt, depth, path):
         src_bs = BatchSt.objects.get(batch=src_batch, version=1)
     except BatchSt.DoesNotExist:
         return None
-    p_white, p_kinds = _zt_white_count(src_bs, depth + 1, path)
+    p_white, p_kinds, p_clean = _zt_white_walk(src_bs, depth + 1, path, memo)
     if p_white is None:
         return None
-    return p_white * ratio, p_kinds | {"谱系兜底"}
+    return p_white * ratio, p_kinds | {"谱系兜底"}, p_clean
 
 
 def cal_zt_big(big_batch: str):
@@ -400,7 +422,7 @@ def cal_zt_big(big_batch: str):
         return
     if big_bs.zt_batch != big_batch:
         big_bs.zt_batch = big_batch
-        big_bs.save(update_fields=["zt_batch"])
+        big_bs.save(update_fields=["zt_batch", "update_time"])
 
     # 从大批侧发现子批: 拆批目标 + 完全由本大批子批合并出的复检批(如 大批-A-1), 支持多级
     known = {big_batch}
@@ -434,7 +456,7 @@ def cal_zt_big(big_batch: str):
             continue
         if t.zt_batch != big_batch:
             t.zt_batch = big_batch
-            t.save(update_fields=["zt_batch"])
+            t.save(update_fields=["zt_batch", "update_time"])
 
     # 分子: 所有归属本大批的批次(检验小批/复检批)外观检验直通合格数之和
     count_zt = 0
@@ -476,7 +498,7 @@ def cal_zt_big(big_batch: str):
         data["直通_白料数"] = None
         data["直通_良率"] = None
     big_bs.data = json.loads(json.dumps(data, cls=MyJSONEncoder))
-    big_bs.save(update_fields=["data"])
+    big_bs.save(update_fields=["data", "update_time"])
 
 
 if __name__ == '__main__':
diff --git a/apps/wpm/services.py b/apps/wpm/services.py
index a9250370..796cbd85 100644
--- a/apps/wpm/services.py
+++ b/apps/wpm/services.py
@@ -38,7 +38,7 @@ def inherit_zt_batch(source: BatchSt, target: BatchSt):
             myLogger.error(f"直通统计-{target.batch}已归属{target.zt_batch}, 与来源{source.batch}的{source.zt_batch}冲突, 不覆盖")
             return
         target.zt_batch = source.zt_batch
-        target.save(update_fields=["zt_batch"])
+        target.save(update_fields=["zt_batch", "update_time"])
 
 def check_wpr_number(number: str):
     return (len(number) >= 5 and 
diff --git a/scripts/correct_zt_gxerp.py b/scripts/correct_zt_gxerp.py
index 266b1590..8bc863e5 100644
--- a/scripts/correct_zt_gxerp.py
+++ b/scripts/correct_zt_gxerp.py
@@ -26,7 +26,7 @@ import json
 from apps.utils.tools import MyJSONEncoder
 
 
-def main(since: str = "2025-06-17", fresh: bool = False):
+def main(since: str = "2026-06-01", fresh: bool = False):
     qs = BatchSt.objects.filter(
         create_time__gte=datetime.strptime(since, "%Y-%m-%d"), version=1
     ).filter(
@@ -42,7 +42,7 @@ def main(since: str = "2025-06-17", fresh: bool = False):
             big = resolve_zt_big(bs, bs.data or {})
             if big and bs.zt_batch != big:
                 bs.zt_batch = big
-                bs.save(update_fields=["zt_batch"])
+                bs.save(update_fields=["zt_batch", "update_time"])
         except Exception as e:
             print(f"[{ind + 1}/{total}] {bs.batch} 归属解析失败: {e}")
             continue
@@ -61,7 +61,7 @@ def main(since: str = "2025-06-17", fresh: bool = False):
                     data = big_bs.data
                     data.pop("直通_白料数", None)
                     big_bs.data = json.loads(json.dumps(data, cls=MyJSONEncoder))
-                    big_bs.save(update_fields=["data"])
+                    big_bs.save(update_fields=["data", "update_time"])
             cal_zt_big(big)
         except Exception as e:
             print(f"{big} 计算失败: {e}")