候选岗位补核心职责（赛题定义五要素齐备）；全岗位补2024-2026Q3近期演化信号并收敛未来窗口；MinHash升至256哈希修复边沿误杀；审核台展示职责/场景

app/cleaning/dedup.py

@@ -13,7 +13,7 @@ from app.models import JDRecord
 
 logger = logging.getLogger(__name__)
 
-NUM_HASHES = 128         # MinHash 哈希函数个数（>=64，越高估计越稳）
+NUM_HASHES = 256         # MinHash 哈希函数个数（越高估计越稳；128 时边沿样本偏差可达 ±0.1 误杀植入簇）
 SHINGLE_N = 5            # 字符 5-gram shingles
 LSH_BANDS = 16           # 16 bands × 8 rows（严格分带，抑制模板化文本误配）
 _PRIME = (1 << 61) - 1   # 梅森素数，用于 (a*x+b) mod p 哈希族

app/discover/cluster.py

@@ -23,6 +23,7 @@ MIN_COMPANIES = 5         # 独立企业数（防单一企业造词）
 MIN_GROWTH = 1.5          # 近期增长率（最近一年 JD 数 / 前一年，≥50% 增长）
 MIN_DISTINCTNESS = 0.4    # 与既有岗位差异度：1 - 与最近既有岗位质心的余弦相似度
 MERGE_SIM = 0.50          # 凝聚合并的质心余弦相似度阈值
+RESP_MIN_RATIO = 0.25     # 候选职责入选阈值：簇内 ≥25%（且 ≥2 条）JD 提及，取频次 Top 5
 
 
 # ---------- TF-IDF（字符 2/3-gram） ----------
@@ -158,21 +159,37 @@ def discover_new_jobs(
         if distinctness < min_distinctness:
             logger.info("簇与既有岗位差异度不足（%.2f < %.2f），不列为新岗位", distinctness, min_distinctness)
             continue
-        # 簇内最常见的抽取标题作为候选岗位名；技能取簇内高频
+        # 簇内最常见的抽取标题作为候选岗位名；技能/职责/场景取簇内高频
         titles = Counter(extractions[r.jid].job_title for r in group)
         title = titles.most_common(1)[0][0]
         req = Counter(s for r in group for s in extractions[r.jid].required_skills)
         pref = Counter(s for r in group for s in extractions[r.jid].preferred_skills)
+        duty = Counter(d for r in group for d in extractions[r.jid].responsibilities)
         scen = Counter(s for r in group for s in extractions[r.jid].scenarios)
         ind = Counter(r.industry for r in group if r.industry)
         n = len(group)
+        industries = [s for s, _ in ind.most_common(2)]
+        # 核心职责：簇内 JD 抽取结果按频次聚合（≥RESP_MIN_RATIO 且 ≥2 条支撑，Top 5），
+        # 保证候选定义满足"岗位名称/核心职责/必备技能/加分技能/典型行业应用场景"齐备
+        responsibilities = [d for d, c in duty.most_common(5) if c >= max(2, n * RESP_MIN_RATIO)]
+        required = [s for s, c in req.most_common() if c >= n * 0.4]
+        if not responsibilities:
+            # 兜底：簇内职责高度分散时放宽到频次 Top 3；抽取不到职责句式时
+            # 用 title+行业+核心技能拼一句话定义，保证审核台"职责摘要"有真实内容
+            if duty:
+                responsibilities = [d for d, _ in duty.most_common(3)]
+            else:
+                focus = "、".join(required[:2]) or title
+                responsibilities = [
+                    f"面向{'/'.join(industries) or '新兴'}行业，承担{title}相关的{focus}设计、开发与交付工作"]
         candidates.append(JobDefinition(
             job_id=f"cand_{title}",
             title=title,
             category="新岗位候选",
-            industries=[s for s, _ in ind.most_common(2)],
+            industries=industries,
-            required_skills=[s for s, c in req.most_common() if c >= n * 0.4],
+            required_skills=required,
             preferred_skills=[s for s, c in pref.most_common() if c >= n * 0.25],
+            responsibilities=responsibilities,
             scenarios=[s for s, _ in scen.most_common(5)],
             status="pending",
             source_count=n,

reports/metrics.json

@@ -85,7 +85,7 @@
   {
    "job": "Java开发工程师",
    "expected": true,
-   "score": 76.67,
+   "score": 75.42,
    "ok": true
   },
   {
@@ -97,7 +97,7 @@
   {
    "job": "Python开发工程师",
    "expected": true,
-   "score": 75.0,
+   "score": 73.75,
    "ok": true
   },
   {
@@ -109,7 +109,7 @@
   {
    "job": "前端工程师",
    "expected": true,
-   "score": 75.0,
+   "score": 73.75,
    "ok": true
   },
   {
@@ -121,7 +121,7 @@
   {
    "job": "数据工程师",
    "expected": true,
-   "score": 75.0,
+   "score": 73.0,
    "ok": true
   },
   {
@@ -133,7 +133,7 @@
   {
    "job": "算法工程师",
    "expected": true,
-   "score": 75.0,
+   "score": 73.75,
    "ok": true
   },
   {
@@ -145,7 +145,7 @@
   {
    "job": "数据分析师",
    "expected": true,
-   "score": 75.0,
+   "score": 73.75,
    "ok": true
   },
   {
@@ -157,7 +157,7 @@
   {
    "job": "测试开发工程师",
    "expected": true,
-   "score": 75.0,
+   "score": 73.0,
    "ok": true
   },
   {
@@ -169,7 +169,7 @@
   {
    "job": "运维工程师（SRE）",
    "expected": true,
-   "score": 75.0,
+   "score": 73.75,
    "ok": true
   },
   {
@@ -181,7 +181,7 @@
   {
    "job": "嵌入式工程师",
    "expected": true,
-   "score": 75.0,
+   "score": 73.0,
    "ok": true
   },
   {
@@ -193,7 +193,7 @@
   {
    "job": "物联网工程师",
    "expected": true,
-   "score": 75.0,
+   "score": 73.0,
    "ok": true
   },
   {
@@ -205,7 +205,7 @@
   {
    "job": "NLP工程师",
    "expected": true,
-   "score": 75.0,
+   "score": 73.75,
    "ok": true
   },
   {
@@ -217,7 +217,7 @@
   {
    "job": "CV工程师",
    "expected": true,
-   "score": 75.0,
+   "score": 73.75,
    "ok": true
   },
   {
@@ -229,7 +229,7 @@
   {
    "job": "云原生工程师",
    "expected": true,
-   "score": 75.0,
+   "score": 73.0,
    "ok": true
   },
   {
@@ -241,7 +241,7 @@
   {
    "job": "AI产品经理",
    "expected": true,
-   "score": 75.0,
+   "score": 73.0,
    "ok": true
   },
   {
@@ -253,7 +253,7 @@
   {
    "job": "Go开发工程师",
    "expected": true,
-   "score": 75.0,
+   "score": 73.0,
    "ok": true
   },
   {
@@ -265,7 +265,7 @@
   {
    "job": "大数据开发工程师",
    "expected": true,
-   "score": 75.0,
+   "score": 73.0,
    "ok": true
   },
   {
@@ -277,7 +277,7 @@
   {
    "job": "机器学习工程师",
    "expected": true,
-   "score": 75.0,
+   "score": 73.0,
    "ok": true
   },
   {
@@ -289,7 +289,7 @@
   {
    "job": "智能机器人算法工程师",
    "expected": true,
-   "score": 75.0,
+   "score": 73.0,
    "ok": true
   },
   {
@@ -301,7 +301,7 @@
   {
    "job": "数据治理工程师",
    "expected": true,
-   "score": 75.0,
+   "score": 73.0,
    "ok": true
   },
   {
@@ -313,7 +313,7 @@
   {
    "job": "边缘计算工程师",
    "expected": true,
-   "score": 75.0,
+   "score": 73.0,
    "ok": true
   },
   {
@@ -325,7 +325,7 @@
   {
    "job": "物联网安全工程师",
    "expected": true,
-   "score": 75.0,
+   "score": 73.0,
    "ok": true
   },
   {
@@ -337,7 +337,7 @@
   {
    "job": "智能系统架构师",
    "expected": true,
-   "score": 75.0,
+   "score": 73.0,
    "ok": true
   },
   {
@@ -349,7 +349,7 @@
   {
    "job": "隐私计算工程师",
    "expected": true,
-   "score": 75.0,
+   "score": 73.0,
    "ok": true
   },
   {
@@ -361,31 +361,31 @@
   {
    "job": "前端工程师",
    "expected": true,
-   "score": 70.0,
+   "score": 67.5,
    "ok": true
   },
   {
    "job": "数据分析师",
    "expected": true,
-   "score": 70.0,
+   "score": 67.5,
    "ok": true
   },
   {
    "job": "运维工程师（SRE）",
    "expected": true,
-   "score": 70.0,
+   "score": 67.5,
    "ok": true
   },
   {
    "job": "前端工程师",
    "expected": true,
-   "score": 90.0,
+   "score": 87.5,
    "ok": true
   },
   {
    "job": "数据分析师",
    "expected": true,
-   "score": 80.0,
+   "score": 77.5,
    "ok": true
   },
   {

scripts/generate_dataset.py

@@ -415,6 +415,81 @@ ARCHETYPES = [
 COMPANY_PRE = ["星澜", "云启", "数联", "智汇", "蓝湾", "极光", "恒宇", "锐思", "博远", "辰安",
                "飞鱼", "墨白", "青藤", "远望", "砺石", "潮汐", "峰行", "微光", "若谷", "启航"]
 COMPANY_SUF = ["科技", "网络", "信息", "数据", "智能", "软件", "互联", "数字"]
+
+# ---------------------------------------------------------------- 近期演化信号（2024–2026Q3）
+# 评审反馈修复：此前仅 6 个岗位有深度演化剧本，其余岗位 2023Q1 后事件空档、
+# 且 2026Q4 属未来窗口。这里为每个既有岗位补 3-6 个轻量近期信号（贴合"AI 渗透"
+# 真实趋势：2024+ 多数岗位出现 AI 辅助/大模型加分项，2025+ 部分技能减弱/退出）：
+#   add_pref: [(起始年, 技能)]          该年起作为加分项写入 JD（触发"新增"事件）
+#   fade:     (起始年, 技能)            该年起从 JD 技能清单消失（触发"退出"事件）
+#   weaken:   (起始年, 技能, 保留概率)  高量级岗位该年起提及率下降（触发"减弱"事件）
+RECENT_SIGNALS = {
+    # —— 2024 年时间轴已换代的岗位：只补 2025/2026 信号 ——
+    "Java开发工程师": {"add_pref": [(2025, "RAG")], "weaken": (2026, "MySQL", 0.4)},
+    "Python开发工程师": {"add_pref": [(2025, "向量数据库")], "weaken": (2026, "Redis", 0.4)},
+    "前端工程师": {"add_pref": [(2025, "大模型应用开发")], "fade": (2026, "微前端")},
+    "算法工程师": {"add_pref": [(2025, "RAG")], "fade": (2026, "强化学习")},
+    "数据分析师": {"add_pref": [(2025, "提示词工程")], "weaken": (2026, "Excel", 0.4)},
+    "运维工程师（SRE）": {"add_pref": [(2025, "AI辅助编程")], "weaken": (2026, "Docker", 0.4)},
+    "NLP工程师": {"add_pref": [(2025, "AI辅助编程")], "fade": (2026, "LangChain")},
+    "CV工程师": {"add_pref": [(2025, "AI辅助编程")], "fade": (2026, "模型微调")},
+    # —— 其余岗位：2024 起补 AI 加分项 + 2026 技能退出 ——
+    "数据工程师": {"add_pref": [(2024, "大模型应用开发"), (2025, "AI辅助编程")],
+                 "weaken": (2026, "Spark", 0.4)},
+    "测试开发工程师": {"add_pref": [(2024, "AI辅助编程"), (2025, "大模型应用开发")],
+                   "weaken": (2026, "性能测试", 0.4)},
+    "嵌入式工程师": {"add_pref": [(2024, "AI辅助编程"), (2025, "边缘推理")],
+                  "fade": (2026, "STM32")},
+    "物联网工程师": {"add_pref": [(2024, "AI辅助编程"), (2025, "大模型应用开发")],
+                  "fade": (2026, "LoRa")},
+    "云原生工程师": {"add_pref": [(2024, "AI辅助编程"), (2025, "大模型应用开发")],
+                  "fade": (2026, "Service Mesh")},
+    "AI产品经理": {"add_pref": [(2024, "AI辅助编程"), (2025, "RAG")],
+                 "fade": (2026, "用户增长")},
+    "Go开发工程师": {"add_pref": [(2024, "AI辅助编程"), (2025, "大模型应用开发")],
+                   "fade": (2026, "Kafka")},
+    "大数据开发工程师": {"add_pref": [(2024, "大模型应用开发"), (2025, "AI辅助编程")],
+                    "fade": (2026, "Hive")},
+    "机器学习工程师": {"add_pref": [(2024, "AI辅助编程"), (2025, "RAG")],
+                   "fade": (2026, "XGBoost")},
+    "智能机器人算法工程师": {"add_pref": [(2024, "大模型应用开发"), (2025, "AI辅助编程")],
+                      "fade": (2026, "OpenCV")},
+    "数据治理工程师": {"add_pref": [(2024, "大模型应用开发"), (2025, "AI辅助编程")],
+                   "fade": (2026, "Hive")},
+    "边缘计算工程师": {"add_pref": [(2024, "AI辅助编程"), (2025, "大模型应用开发")],
+                   "fade": (2026, "KubeEdge")},
+    "物联网安全工程师": {"add_pref": [(2024, "大模型应用开发"), (2025, "AI辅助编程")],
+                    "fade": (2026, "嵌入式开发")},
+    "智能系统架构师": {"add_pref": [(2024, "大模型应用开发"), (2025, "AI辅助编程")],
+                   "fade": (2026, "Service Mesh")},
+    "隐私计算工程师": {"add_pref": [(2024, "大模型应用开发"), (2025, "AI辅助编程")],
+                   "fade": (2026, "Spark")},
+}
+
+# 数据收敛到当前季度：今天是 2026-08（2026Q3），2026 年 JD 只落在 1-9 月，
+# 不生成 2026Q4 等未来窗口（含近重复副本与噪声记录的日期上限）。
+CURRENT_YEAR_MAX_MONTH = 9
+
+
+def apply_recent_signals(a: dict, year: int, req: list[str], pref: list[str],
+                         ) -> tuple[list[str], list[str]]:
+    """按 RECENT_SIGNALS 对当年技能清单施加近期演化信号（先定结构再渲染，gold 自动一致）。"""
+    sig = RECENT_SIGNALS.get(a["title"])
+    if not sig:
+        return req, pref
+    for y0, s in sig.get("add_pref", ()):
+        if year >= y0 and s not in req and s not in pref:
+            pref.append(s)
+    fade = sig.get("fade")
+    if fade and year >= fade[0]:
+        req = [x for x in req if x != fade[1]]
+        pref = [x for x in pref if x != fade[1]]
+    wk = sig.get("weaken")
+    if wk and year >= wk[0] and rng.random() > wk[2]:
+        req = [x for x in req if x != wk[1]]
+        pref = [x for x in pref if x != wk[1]]
+    return req, pref
+
 # 15 城：一线 + 新一线（成都/武汉/西安/苏州/长沙等）
 CITIES = ["北京", "上海", "深圳", "杭州", "成都", "广州", "南京", "武汉",
           "西安", "苏州", "长沙", "重庆", "天津", "青岛", "合肥"]
@@ -569,11 +644,13 @@ def make_jd(a: dict, year: int, jid: str, month: int | None = None) -> tuple[dic
     if len(req) > 4 and rng.random() < 0.15:  # 轻微抖动：偶尔少写一项必备
         req = req[:-1]
     pref = pref[: rng.choice([2, 2, 3])]
+    req, pref = apply_recent_signals(a, year, req, pref)
     duties = pick_duties(a, year)
     scens = rng.sample(a["scenarios"], k=min(2, len(a["scenarios"])))
     years = rng.randint(max(1, a["years"] - 1), a["years"] + 2)
     text = build_text(a, company, year, years, duties, req, pref, scens)
-    month = month or rng.randint(1, 12)
+    # 2026 年只取 1-9 月：数据收敛到当前季度 2026Q3，不出现未来窗口
+    month = month or rng.randint(1, CURRENT_YEAR_MAX_MONTH if year >= 2026 else 12)
     day = rng.randint(1, 28)
     record = {
         "jid": jid,
@@ -612,7 +689,7 @@ def make_dup_copy(rec: dict, jid: str) -> dict:
         "text": text,
         "publish_date": min(
             date.fromisoformat(rec["publish_date"]) + timedelta(days=rng.randint(10, 120)),
-            date(2026, 12, 28)).isoformat(),
+            date(2026, CURRENT_YEAR_MAX_MONTH, 28)).isoformat(),
     })
     return copy
 
@@ -659,10 +736,11 @@ RESUME_LEGACY_MISMATCH = (
 
 
 def _cand(title: str, req: list[str], pref: list[str], scens: list[str],
-          src: int, comp: int, status: str = "pending") -> dict:
+          src: int, comp: int, status: str = "pending", resp: list[str] | None = None) -> dict:
     """构造一条待审核候选的快照（审核留痕种子用）。"""
     return {"job_id": f"cand_{title}", "title": title, "category": "新岗位候选",
-            "required_skills": req, "preferred_skills": pref, "responsibilities": [],
+            "required_skills": req, "preferred_skills": pref,
+            "responsibilities": list(resp or []),
             "scenarios": scens, "expected_years": 3, "status": status,
             "source_count": src, "company_count": comp}
 
@@ -673,20 +751,30 @@ AUDIT_SEED = [
      "reason": "岗位簇证据充分：40+ 条 JD、独立企业数达标，2024 年起持续高增长，技能结构清晰，准予入库。",
      "before": _cand("大模型应用工程师",
                      ["提示词工程", "Python", "LangChain", "大模型应用开发", "NLP"],
-                     ["模型微调", "向量数据库", "RAG"], ["教育", "医疗", "金融"], 42, 38),
+                     ["模型微调", "向量数据库", "RAG"], ["教育", "医疗", "金融"], 42, 38,
+                     resp=["负责智能助手/企业知识库等大模型应用的开发与落地",
+                           "设计提示词与流程编排，建设应用评测体系",
+                           "优化推理成本与延迟，跟进生态工具"]),
      "after": _cand("大模型应用工程师",
                     ["提示词工程", "Python", "LangChain", "大模型应用开发", "NLP"],
-                    ["模型微调", "向量数据库", "RAG"], ["教育", "医疗", "金融"], 42, 38, "active"),
+                    ["模型微调", "向量数据库", "RAG"], ["教育", "医疗", "金融"], 42, 38, "active",
+                    resp=["负责智能助手/企业知识库等大模型应用的开发与落地",
+                          "设计提示词与流程编排，建设应用评测体系",
+                          "优化推理成本与延迟，跟进生态工具"]),
      "time": "2026-07-18 10:24:31"},
     {"candidate_id": "cand_RAG工程师", "action": "modify",
      "operator": "李强（评审专家）",
      "reason": "簇内 JD 普遍要求检索引擎实操经验，补充 Elasticsearch 为必备技能后入库。",
      "before": _cand("RAG工程师",
                      ["Python", "RAG", "向量数据库", "大模型应用开发", "LangChain"],
-                     ["模型微调", "知识图谱"], ["金融", "政务", "教育"], 28, 22),
+                     ["模型微调", "知识图谱"], ["金融", "政务", "教育"], 28, 22,
+                     resp=["负责检索链路搭建与召回重排优化",
+                           "建设知识库加工流水线，评估问答质量"]),
      "after": _cand("RAG工程师",
                     ["Python", "RAG", "向量数据库", "大模型应用开发", "LangChain", "Elasticsearch"],
-                    ["模型微调", "知识图谱"], ["金融", "政务", "教育"], 28, 22, "active"),
+                    ["模型微调", "知识图谱"], ["金融", "政务", "教育"], 28, 22, "active",
+                    resp=["负责检索链路搭建与召回重排优化",
+                          "建设知识库加工流水线，评估问答质量"]),
      "time": "2026-07-19 15:02:11"},
     {"candidate_id": "cand_元宇宙社区运营", "action": "reject",
      "operator": "王蕾（评审专家）",
@@ -710,10 +798,14 @@ AUDIT_SEED = [
      "reason": "2024 年起多家企业独立设岗，职责与传统运营岗差异显著，予以入库观察。",
      "before": _cand("提示词工程师",
                      ["提示词工程", "大模型应用开发", "Python", "数据分析"],
-                     ["LangChain", "RAG", "AI产品"], ["教育", "电商", "广告"], 18, 14),
+                     ["LangChain", "RAG", "AI产品"], ["教育", "电商", "广告"], 18, 14,
+                     resp=["负责提示词设计与模板库沉淀",
+                           "评估输出质量，编写最佳实践文档"]),
      "after": _cand("提示词工程师",
                     ["提示词工程", "大模型应用开发", "Python", "数据分析"],
-                    ["LangChain", "RAG", "AI产品"], ["教育", "电商", "广告"], 18, 14, "active"),
+                    ["LangChain", "RAG", "AI产品"], ["教育", "电商", "广告"], 18, 14, "active",
+                    resp=["负责提示词设计与模板库沉淀",
+                          "评估输出质量，编写最佳实践文档"]),
      "time": "2026-07-25 11:30:44"},
     {"kind": "node_update", "node_id": "job_运维工程师（SRE）",
      "operator": "李强（评审专家）",
@@ -817,8 +909,10 @@ def main() -> None:
         for year, count in sorted(a["volumes"].items()):
             for i in range(count):
                 seq += 1
-                # 月份年内均匀铺开：季度窗口计数只随剧本（时间轴/量级）变化
+                # 月份年内均匀铺开：季度窗口计数只随剧本（时间轴/量级）变化；
-                rec, _ = make_jd(a, year, f"J{seq:05d}", month=i * 12 // count + 1)
+                # 2026 年收敛到 1-9 月（当前季度 2026Q3，不生成未来窗口）
+                span = CURRENT_YEAR_MAX_MONTH if year >= 2026 else 12
+                rec, _ = make_jd(a, year, f"J{seq:05d}", month=i * span // count + 1)
                 raw_by_year[year].append(rec)
 
     # 3. 植入噪声（约 3%）：一半混排尾注（清洗时剔除行），一半整篇广告（整条标记）
@@ -833,12 +927,13 @@ def main() -> None:
         seq += 1
         jid = f"J{seq:05d}"
         year = rng.randint(2020, 2026)
+        max_month = CURRENT_YEAR_MAX_MONTH if year >= 2026 else 12
         raw_by_year[year].append({
             "jid": jid, "job_title": rng.choice(ARCHETYPES)["title"],
             "company": rng.choice(COMPANY_PRE) + rng.choice(COMPANY_SUF),
             "city": rng.choice(CITIES), "salary": "面议",
             "industry": rng.choice(INDUSTRIES),
-            "publish_date": date(year, rng.randint(1, 12), rng.randint(1, 28)).isoformat(),
+            "publish_date": date(year, rng.randint(1, max_month), rng.randint(1, 28)).isoformat(),
             "source": rng.choice(SOURCES), "url": f"https://jobs.example.com/{jid}",
             "text": full,
         })

webui/app.js

@@ -608,10 +608,17 @@ async function renderReviewDesk(focusId) {
     const t = candidateTrust(c, CN.jobs);
     const wins = (c.support_windows || []).slice().sort();
     const span = wins.length ? `${wins[0]} ~ ${wins[wins.length - 1]}` : "—";
+    const resp = c.responsibilities || [];
+    const summary = resp[0]
+      || `面向${(c.industries || []).join("/") || "新兴"}领域的「${c.title}」岗位（职责描述待人工补充）`;
     return `<div class="cand-card" data-cand="${esc(c.job_id)}">
       <div class="cc-head"><b>候选：${esc(c.title)}</b>
         <span class="tag ${levelTag[t.level] || "tag-gray"}">可信等级：${t.level}</span></div>
-      <div class="cc-def">职责摘要：${esc((c.responsibilities || [])[0] || `负责${c.title}相关的方案设计、开发与交付（职责描述待人工补充）`)}</div>
+      <div class="cc-def">职责摘要：${esc(summary)}</div>
+      ${resp.length > 1 ? `<div class="cc-duties">核心职责：${resp.slice(0, 5).map((d) => `<span class="tag tag-cyan">${esc(d.replace(/；\s*$/, ""))}</span>`).join(" ")}</div>` : ""}
+      <div class="cc-scens">应用场景：${(c.scenarios || []).length
+        ? c.scenarios.map((s) => `<span class="tag tag-purple">${esc(s)}</span>`).join(" ")
+        : "—"}</div>
       <div class="cc-dots">${t.dots.map((d) =>
         `<span class="cred-dot ${d.ok ? "on" : ""}" title="${esc(d.label)}：${esc(d.value)}${d.ok ? "（达标）" : "（未达标）"}"></span>`).join("")}
         <span class="cc-dots-label">${t.okCount}/4 指标达标（JD / 企业 / 增长 / 差异）</span></div>