fix:归属解析支持多级拆批爬链与未拆批直检大批
- 孙批沿拆批链爬到顶层大批, 修复中间子批被错锁为大批 - 复检批合批来源统一按爬链顶层判定, 修复"来源大批不一致"误报 - 来源均为炉料链且批号为大批格式时自身即大批(未拆批直接检验); 不良集中池等非大批格式不归属 - correct脚本--fresh忽略旧锁重新解析, 并清理错误的大批标记与直通键 Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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@ -229,33 +229,60 @@ def _zt_split_parents(batchst: BatchSt):
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return {e.source.batch for e in qs}
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def resolve_zt_big(batchst: BatchSt, data: dict):
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"""确定检验小批所属的大批号, 无法确定返回 None"""
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if batchst.zt_batch:
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def _zt_climb_split_top(batchst: BatchSt, max_hops=4):
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"""沿拆批链向上爬到顶层批(多级拆批的孙批归属顶层大批)
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无拆批父级返回 None; 多个拆批父级(异常)停在当前层。
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"""
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node = batchst
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top = None
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for _ in range(max_hops):
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ps = _zt_split_parents(node)
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if len(ps) != 1:
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break
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top = ps.pop()
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parent = BatchSt.objects.filter(batch=top, version=1).first()
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if parent is None:
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break
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node = parent
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return top
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def resolve_zt_big(batchst: BatchSt, data: dict, use_lock=True):
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"""确定检验小批所属的大批号, 无法确定返回 None
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use_lock=False 时忽略已锁定的 zt_batch, 重新从谱系解析(供回刷纠错)。
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"""
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if use_lock and batchst.zt_batch:
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return batchst.zt_batch
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if not any(k in data for k in ("尺寸检验_count_real", "外观检验_count_real")):
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return None
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parents = _zt_split_parents(batchst)
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if len(parents) == 1:
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return parents.pop()
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if len(parents) > 1:
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myLogger.error(f"直通统计-{batchst.batch}存在多个拆批来源{parents}, 无法归属大批")
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return None
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# 合批复检批(如 大批-A-1): 所有合批来源须归属同一大批
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top = _zt_climb_split_top(batchst)
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if top:
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return top
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# 合批复检批(如 大批-A-1): 所有可归属的合批来源须属于同一大批
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merge_srcs = [e.source for e in BatchLog.objects.filter(target=batchst, relation_type="merge").select_related("source")]
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if merge_srcs:
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bigs = set()
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for s in merge_srcs:
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if s.zt_batch:
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if s.zt_batch and s.zt_batch != s.batch:
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bigs.add(s.zt_batch)
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else:
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ps = _zt_split_parents(s)
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bigs.add(ps.pop() if len(ps) == 1 else None)
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if len(bigs) == 1 and None not in bigs:
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t = _zt_climb_split_top(s)
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if t:
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bigs.add(t)
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# 无拆批谱系的来源(炉料链/改号链)不参与判定
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if len(bigs) == 1:
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return bigs.pop()
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myLogger.error(f"直通统计-{batchst.batch}合批来源大批不一致{bigs}, 无法归属")
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if len(bigs) > 1:
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myLogger.error(f"直通统计-{batchst.batch}合批来源大批不一致{bigs}, 无法归属")
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return None
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# 来源均为炉料链: 合批改名诞生的大批未拆批直接检验, 自身即大批;
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# 非大批格式(如不良集中池)不归属
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if ZT_BIG_BATCH_RE.match(batchst.batch):
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return batchst.batch
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return None
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# 无任何拆合批上游: 自身即大批(未拆批直接检验)
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# 无任何拆合批上游: 自身即大批(未拆批直接检验/外购直检)
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return batchst.batch
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@ -26,31 +26,59 @@ import json
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from apps.utils.tools import MyJSONEncoder
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def main(since: str = "2026-06-01", fresh: bool = False):
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def main(since: str = "2025-06-17", fresh: bool = False):
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cond = Q(data__has_key="尺寸检验_日期") | Q(data__has_key="外观检验_日期")
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if fresh:
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# 纠错模式需覆盖曾被(错误)锁定/计算过的批
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cond = cond | Q(zt_batch__isnull=False) | Q(data__has_key="直通_良率")
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qs = BatchSt.objects.filter(
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create_time__gte=datetime.strptime(since, "%Y-%m-%d"), version=1
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).filter(
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Q(data__has_key="尺寸检验_日期") | Q(data__has_key="外观检验_日期")
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).order_by("create_time")
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).filter(cond).order_by("create_time")
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total = qs.count()
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print(f"检验批 {total} 个, 开始解析大批归属")
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print(f"检验批 {total} 个, 开始解析大批归属 (fresh={fresh})", flush=True)
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bigs = [] # 保序去重
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seen = set()
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for ind, bs in enumerate(qs.iterator()):
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try:
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big = resolve_zt_big(bs, bs.data or {})
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# fresh 模式忽略旧锁, 从谱系重新解析(纠正多级拆批错锁)
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big = resolve_zt_big(bs, bs.data or {}, use_lock=not fresh)
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if big and bs.zt_batch != big:
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bs.zt_batch = big
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bs.save(update_fields=["zt_batch", "update_time"])
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except Exception as e:
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print(f"[{ind + 1}/{total}] {bs.batch} 归属解析失败: {e}")
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print(f"[{ind + 1}/{total}] {bs.batch} 归属解析失败: {e}", flush=True)
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continue
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if big and big not in seen:
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seen.add(big)
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bigs.append(big)
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if (ind + 1) % 1000 == 0:
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print(f"归属解析 {ind + 1}/{total}")
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print(f"归属解析 {ind + 1}/{total}", flush=True)
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if fresh:
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# 清理: 曾被错当成大批计算过直通字段、如今无任何子批指向的批,
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# 剥离其直通键并解除自指锁定, 交由后续大批计算重新归属
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cleaned = 0
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for bs in BatchSt.objects.filter(
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create_time__gte=datetime.strptime(since, "%Y-%m-%d"), version=1,
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data__has_key="直通_良率",
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).iterator():
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if bs.batch in seen:
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continue
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if BatchSt.objects.filter(zt_batch=bs.batch, version=1).exclude(id=bs.id).exists():
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continue
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data = bs.data
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for k in ["直通_子批次", "直通_总合格数", "直通_白料数", "直通_良率", "直通_口径"]:
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data.pop(k, None)
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bs.data = json.loads(json.dumps(data, cls=MyJSONEncoder))
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if bs.zt_batch == bs.batch:
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bs.zt_batch = None
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bs.save(update_fields=["data", "zt_batch", "update_time"])
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else:
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bs.save(update_fields=["data", "update_time"])
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cleaned += 1
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print(f"清理错误大批标记: {bs.batch}", flush=True)
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print(f"清理 {cleaned} 个", flush=True)
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print(f"共 {len(bigs)} 个大批, 开始计算直通良率", flush=True)
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for ind, big in enumerate(bigs):
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