From 5c3efbff940bf58277bc7a60ffb7f2b3416788ef Mon Sep 17 00:00:00 2001
From: zlt <zlt@zlt.com>
Date: Wed, 5 Aug 2026 10:24:44 +0800
Subject: [PATCH] =?UTF-8?q?=E4=BA=A7=E4=B8=9A=E9=93=BE=E7=BB=86=E5=8C=96?=
 =?UTF-8?q?=E9=80=BB=E8=BE=91?=
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---
 chain_diag.py | 24 ++++++++++++++----------
 1 file changed, 14 insertions(+), 10 deletions(-)

diff --git a/chain_diag.py b/chain_diag.py
index b6fa508..d13f341 100644
--- a/chain_diag.py
+++ b/chain_diag.py
@@ -242,7 +242,7 @@ def diag_matrix(yml: dict, mem: pd.DataFrame, chain2track, kw, outdir: str) -> N
 
 
 # ---------------------------------------------------------------- 读数四
-def diag_misskill(segs: dict, outdir: str) -> None:
+def diag_misskill(mem: pd.DataFrame, outdir: str) -> None:
     d = db.read_mysql("factor", "SELECT MAX(trade_date) d FROM t_factor_akg_gate")
     v = None if d.empty else d.iloc[0, 0]
     if v is None or pd.isna(v):
@@ -256,13 +256,17 @@ def diag_misskill(segs: dict, outdir: str) -> None:
     gate = {str(r.stock_code).strip(): float(r.factor_value) for r in g.itertuples()}
     upside = {str(r.stock_code).strip(): float(r.factor_value) for r in up.itertuples()}
 
+    # 图谱证据按"该股自己的成员边"逐行归集——链名取本股边上的 chain 修饰。
+    # 首版从环节聚合继承整个环节的链名集合，串味成"3D打印、6G"满屏（08-05 实测），勿回退。
     stock_ev: dict[str, dict] = {}       # 前缀码 → 图谱证据
-    for seg, dd in segs.items():
-        for ts in dd["listed_set"]:
-            k = common.to_prefix(ts)
-            e = stock_ev.setdefault(k, {"name": "", "segs": [], "chains": set()})
-            e["segs"].append(seg)
-            e["chains"] |= set(dd["chains"])
+    for r in mem.itertuples():
+        if pd.isna(r.ts_code) or not str(r.ts_code).strip():
+            continue
+        k = common.to_prefix(str(r.ts_code).strip())
+        e = stock_ev.setdefault(k, {"name": "", "segs": set(), "chains": set()})
+        e["segs"].add(r.segment_name)
+        if r.chain:
+            e["chains"].add(r.chain)
     names = {}
     try:
         pools = db.read_pg("SELECT members FROM industry_pools")
@@ -281,8 +285,8 @@ def diag_misskill(segs: dict, outdir: str) -> None:
         e = stock_ev[k]
         nm = names.get(k, "")
         risk = "风险股" if re.match(r"^(\*?S?ST|退市)", nm.replace(" ", "")) else ""
-        return (k, nm, risk, len(set(e["segs"])),
-                "、".join(sorted(set(e["segs"]))[:3]),
+        return (k, nm, risk, len(e["segs"]),
+                "、".join(sorted(e["segs"])[:3]),
                 "、".join(sorted(e["chains"])[:3]))
 
     a_rows = [(_row(k) + (round(upside[k], 4),))
@@ -459,7 +463,7 @@ def main() -> int:
         ("读数一", lambda: diag_unordered(segs, edg, seg2track, chain2track, outdir)),
         ("读数二", lambda: diag_unmapped(segs, seg2track, chain2track, track_all, outdir)),
         ("读数三", lambda: diag_matrix(yml, mem, chain2track, kw, outdir)),
-        ("读数四", lambda: diag_misskill(segs, outdir)),
+        ("读数四", lambda: diag_misskill(mem, outdir)),
         ("读数五", lambda: diag_fragmentation(segs, mem, edg, outdir)),
         ("读数六", lambda: diag_research(yml, mem, edg, kw, outdir)),
     ]: