全岗位扫描横条配色柔和化（与图表语义色统一为低饱和）

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>
@@ -742,56 +749,42 @@ const STEADY_COLOR = "#86909c";   // 持续：中性蓝灰
 let evoEvents = [];
 
 /* 全部岗位 × 季度 演化热力图 */
-function renderEvoHeatmap() {
+function renderEvoScan() {
-  const box = $("#evo-heatmap");
+  const box = $("#evo-scan");
   const events = (CN.graphData && CN.graphData.events) || [];
   const jobs = CN.jobs || [];
   if (!events.length || !jobs.length) {
     box.innerHTML = `<div class="empty small">暂无演化事件数据</div>`;
     return;
   }
-  const windows = [...new Set(events.map((e) => e.window))].sort().slice(-12);   // 全岗位扫描仅近 12 季度
+  const winSet = new Set([...new Set(events.map((e) => e.window))].sort().slice(-12));
-  // 统计每个岗位每季度事件数与类型分布
+  const per = {};
-  const cell = {};
   events.forEach((e) => {
-    const k = `${e.job_id}|${e.window}`;
+    if (!winSet.has(e.window)) return;
-    cell[k] = cell[k] || { n: 0, types: {} };
+    const p = per[e.job_id] = per[e.job_id] || { add: 0, out: 0, other: 0 };
-    cell[k].n += 1;
+    if (e.change_type === "新增") p.add++;
-    cell[k].types[e.change_type] = (cell[k].types[e.change_type] || 0) + 1;
+    else if (e.change_type === "退出") p.out++;
+    else p.other++;
   });
-  // 按总事件数降序排列岗位
+  const rows = jobs.map((j) => ({ j, ...(per[j.job_id] || { add: 0, out: 0, other: 0 }) }))
-  const totals = {};
+    .map((r) => ({ ...r, total: r.add + r.out + r.other }))
-  events.forEach((e) => { totals[e.job_id] = (totals[e.job_id] || 0) + 1; });
+    .filter((r) => r.total > 0)
-  const rows = [...jobs].sort((a, b) => (totals[b.job_id] || 0) - (totals[a.job_id] || 0));
+    .sort((a, b) => b.total - a.total);
-  const labelEvery = Math.ceil(windows.length / 10);
+  const max = Math.max(1, ...rows.map((r) => r.total));
-  // 色阶：开方缩放 + 12 封顶，避免个别高峰季度（如 57 件）把其余格子压到看不清
+  box.innerHTML = rows.map((r) => `
-  const alphaOf = (n) => 0.18 + 0.82 * Math.sqrt(Math.min(n, 12) / 12);
+    <div class="scan-row" data-job="${esc(r.j.job_id)}"
-  box.innerHTML = `
+         title="${esc(r.j.title)}：新增 ${r.add} · 退出 ${r.out} · 增强/减弱 ${r.other}（点击进岗位报告）">
-    <div class="hm-grid" style="grid-template-columns: 132px repeat(${windows.length}, 1fr)">
+      <span class="scan-label">${esc(r.j.title)}</span>
-      <div class="hm-corner"></div>
+      <span class="scan-track"><i class="scan-bar" style="width:${(r.add / max) * 100}%;background:#7bc8a4"></i><i class="scan-bar" style="width:${(r.out / max) * 100}%;background:#d99a91"></i><i class="scan-bar" style="width:${(r.other / max) * 100}%;background:#8f9ff0"></i></span>
-      ${windows.map((w, i) => `<div class="hm-col-label">${i % labelEvery === 0 ? esc(w) : ""}</div>`).join("")}
+      <span class="scan-num">${r.total}</span>
-      ${rows.map((j) => `
+    </div>`).join("") + `
-        <div class="hm-row-label ${j.job_id === $("#evo-job-select").value ? "active" : ""}"
+    <div class="hm-legend"><span>横条构成：</span><i class="hm-l" style="background:#7bc8a4"></i>新增技能
-             data-job="${esc(j.job_id)}" title="点击查看「${esc(j.title)}」演化详情">${esc(j.title)}</div>
+      <i class="hm-l" style="background:#d99a91"></i>退出技能
-        ${windows.map((w) => {
+      <i class="hm-l" style="background:#8f9ff0"></i>增强/减弱
-          const c = cell[`${j.job_id}|${w}`];
+      <span class="text-weak">（近 12 季度，点击行进入该岗位报告）</span></div>`;
-          const tip = c
+  box.querySelectorAll(".scan-row").forEach((el) => {
-            ? `${j.title} · ${w}：${c.n} 个事件（${Object.entries(c.types).map(([t, n]) => `${t} ${n}`).join("，")}）`
-            : `${j.title} · ${w}：无事件`;
-          return `<div class="hm-cell" data-n="${c ? c.n : 0}" title="${esc(tip)}"
-            style="background:${c ? `rgba(47,84,235,${alphaOf(c.n).toFixed(3)})` : "var(--bg-page)"}"></div>`;
-        }).join("")}`).join("")}
-    </div>
-    <div class="hm-legend">
-      <span>事件数：</span><i class="hm-l" style="background:rgba(47,84,235,0.12)"></i>少
-      <i class="hm-l" style="background:rgba(47,84,235,0.5)"></i>
-      <i class="hm-l" style="background:rgba(47,84,235,1)"></i>多
-      <span class="text-weak">（悬停查看类型分布，点击行标签切换岗位）</span>
-    </div>`;
-  box.querySelectorAll(".hm-row-label").forEach((el) => {
     el.onclick = () => {
       $("#evo-job-select").value = el.dataset.job;
-      /* 点击行 → 转入该岗位报告（默认 Tab） */
       $$("#evo-tabs .match-tab").forEach((x) => x.classList.toggle("active", x.dataset.evotab === "report"));
       $("#evo-tab-report").classList.remove("hidden");
       $("#evo-tab-scan").classList.add("hidden");
@@ -807,7 +800,7 @@ $$("#evo-tabs .match-tab").forEach((b) => {
     const tab = b.dataset.evotab;
     $("#evo-tab-report").classList.toggle("hidden", tab !== "report");
     $("#evo-tab-scan").classList.toggle("hidden", tab !== "scan");
-    if (tab === "scan") renderEvoHeatmap();
+    if (tab === "scan") renderEvoScan();
   };
 });
 
@@ -957,7 +950,6 @@ function drawHeat() {
 function renderEvolution() {
   const gran = $$("#evo-gran button").find((b) => b.classList.contains("active"))?.dataset.gran || "quarter";
   drawStackBars($("#evo-stack-canvas"), evoEvents);
-  renderEvoGantt($("#evo-job-select").value);
   renderEvoReplace($("#evo-job-select").value);
   /* 热度走势：每季度实际生效的技能关系数（按 REQUIRES 边的生效区间统计） */
   const jobEdges = ((CN.graphData || {}).edges || [])
@@ -1071,62 +1063,6 @@ function drawStackBars(canvas, events) {
 
 /* 能力生命线（Swimlane）：每个核心技能一行（近 8 季度），线段=生效区间，圆点=变更事件。
  * 行状态取该技能最近一次事件：新增=青绿 / 增强=蓝紫 / 减弱=琥珀 / 退出=砖红 / 持续=蓝灰 */
-function renderEvoGantt(jobId) {
-  const box = $("#evo-gantt");
-  const g = CN.graphData || {};
-  const allEvents = g.events || [];
-  const windows = [...new Set(allEvents.map((e) => e.window))].sort().slice(-8);
-  const edges = (g.edges || []).filter((e) => e.source === jobId && e.rel === "REQUIRES" && (e.intervals || []).length);
-  if (!windows.length || !edges.length) {
-    box.innerHTML = `<div class="empty small">暂无该岗位的能力生命线数据</div>`;
-    return;
-  }
-  /* Top 12：当前仍生效优先，按当前需求指数降序 */
-  const alive = (e) => e.intervals.some(([s, en]) => en === null);
-  const rows = edges.slice().sort((a, b) =>
-    ((alive(b) ? 1 : 0) - (alive(a) ? 1 : 0)) || ((b.weight || 0) - (a.weight || 0))).slice(0, 12);
-  const dots = {};
-  evoEvents.forEach((e) => { dots[`${e.skill}|${e.window}`] = e.change_type; });
-  const lastWin = windows[windows.length - 1];
-  const recentWins = windows.slice(-4);
-  const rowMeta = (edge) => {
-    const activeNow = edge.intervals.some(([s, en]) => s <= lastWin && (en === null || lastWin < en));
-    if (!activeNow) return { label: "退出", color: CHANGE_META["退出"].color };
-    const lastEv = evoEvents.filter((e) => e.skill === edge.target && recentWins.includes(e.window)).at(-1);
-    if (lastEv) return { label: lastEv.change_type, color: (CHANGE_META[lastEv.change_type] || {}).color || STEADY_COLOR };
-    return { label: edge.importance === "required" ? "核心" : "加分", color: STEADY_COLOR };
-  };
-  const cellHtml = (edge, w, color) => {
-    const active = edge.intervals.some(([s, en]) => s <= w && (en === null || w < en));
-    const dotType = dots[`${edge.target}|${w}`];
-    const dot = dotType
-      ? `<i class="sl-dot" style="background:${(CHANGE_META[dotType] || {}).color || "#999"}" title="${w} ${dotType}"></i>` : "";
-    return `<div class="sl-cell" title="${esc(edge.target)} · ${esc(w)}${active ? "（生效中）" : ""}">
-      ${active ? `<div class="sl-seg" style="background:${color}"></div>` : ""}${dot}</div>`;
-  };
-  box.innerHTML = `
-    <div class="sl-grid" style="grid-template-columns: 128px repeat(${windows.length}, 1fr) 56px">
-      <div class="hm-corner"></div>
-      ${windows.map((w) => `<div class="hm-col-label">${esc(w.slice(2))}</div>`).join("")}
-      <div class="hm-col-label">状态</div>
-      ${rows.map((e) => {
-        const m = rowMeta(e);
-        return `<div class="gt-label" data-skill="${esc(e.target)}" title="点击跳转图谱节点">${esc(e.target)}</div>
-        ${windows.map((w) => cellHtml(e, w, m.color)).join("")}
-        <div class="sl-status" style="color:${m.color}">${m.label}</div>`;
-      }).join("")}
-    </div>
-    <div class="hm-legend">
-      <span>行状态：</span>
-      ${Object.entries(CHANGE_META).map(([t, m]) => `<i class="hm-l" style="background:${m.color}"></i>${t}`).join("")}
-      <i class="hm-l" style="background:${STEADY_COLOR}"></i>持续
-      <span class="text-weak">（圆点 = 该季度变更事件，点击技能名跳转图谱）</span>
-    </div>`;
-  box.querySelectorAll(".gt-label").forEach((el) => {
-    el.onclick = () => { switchView("graph"); if (CN.gotoNode) CN.gotoNode(el.dataset.skill); };
-  });
-}
-
 /* 能力更替流：旧核心（已退出必备） → 持续能力 → 新核心（近 4 季度新进入） */
 function renderEvoReplace(jobId) {
   const box = $("#evo-replace");
@@ -1518,9 +1454,9 @@ function drawRadar(canvas, dims) {
 const batchState = { files: [], result: null };
 
 /* 子页签切换 */
-$$(".match-tab").forEach((b) => {
+$$("#view-match .match-tab").forEach((b) => {
   b.onclick = () => {
-    $$(".match-tab").forEach((x) => x.classList.toggle("active", x === b));
+    $$("#view-match .match-tab").forEach((x) => x.classList.toggle("active", x === b));
     $("#match-single").classList.toggle("hidden", b.dataset.mtab !== "single");
     $("#match-batch").classList.toggle("hidden", b.dataset.mtab !== "batch");
   };
@@ -1977,7 +1913,7 @@ const DEMO_STEPS = [
     desc: "载入示例简历 → 解析确认 → 匹配结论 / 核心缺口 / 可迁移能力",
     go: async () => {
       switchView("match");
-      $$(".match-tab").forEach((x) => x.classList.toggle("active", x.dataset.mtab === "single"));
+      $$("#view-match .match-tab").forEach((x) => x.classList.toggle("active", x.dataset.mtab === "single"));
       $("#match-single").classList.remove("hidden");
       $("#match-batch").classList.add("hidden");
       $("#match-job-select").value = DEMO_JOB;
@@ -2314,7 +2250,7 @@ window.addEventListener("resize", () => {
     if (pathGoto) pathGoto.onclick = () => switchView("match");
     await loadDashboard();          // 先加载图谱缓存（演化页甘特图依赖）
     await loadEvolution();
-    renderEvoHeatmap();
+    renderEvoScan();
     /* 分享链接：#view=graph&node=xxx */
     const hash = new URLSearchParams(location.hash.slice(1));
     if (hash.get("view")) switchView(hash.get("view"));

webui/index.html

@@ -298,10 +298,10 @@
             </div>
 
             <div id="evo-tab-scan" class="hidden">
-              <!-- 全部岗位演化热力图：仅近 12 季度，无事件格子置为极浅色 -->
+              <!-- 全岗位扫描：近 12 季度各岗位变化量横条排行（比热力图直观） -->
-              <div class="evo-chart-title text-aux text-secondary">全部岗位演化热力图 · 近 12 季度（每格 = 该季度演化事件数，点击行切换岗位并回到岗位报告）</div>
+              <div class="evo-chart-title text-aux text-secondary">全岗位扫描 · 近 12 季度变化量排行（点击行进入该岗位报告）</div>
-              <div class="evo-chart-desc">怎么看：每行一个岗位、每列一个季度，颜色越深说明该岗位当季能力变化越多。用于快速发现"哪些岗位正在剧烈变化"；点击行标签进入该岗位的详细报告。</div>
+              <div class="evo-chart-desc">怎么看：每行一个岗位，横条长度=近 12 个季度的能力变化总量；绿色段=新增的技能，红色段=退出的技能。条越长说明这个岗位的能力要求变化越剧烈。</div>
-              <div class="evo-heatmap" id="evo-heatmap"><div class="empty small">加载中…</div></div>
+              <div class="evo-scan" id="evo-scan"><div class="empty small">加载中…</div></div>
             </div>
 
             <div id="evo-tab-report">
@@ -313,11 +313,6 @@
             <div class="evo-chart-desc">是什么：系统根据近两个季度的招聘 JD 自动归纳出的能力变化结论。新增=以前不要求、现在要求；增强=要求变重；减弱=要求变少；退出=不再要求。每条结论都可追溯到原始 JD 证据。</div>
             <div class="evo-summary" id="evo-summary"><div class="empty small">加载中…</div></div>
 
-            <!-- 能力生命线（Swimlane）：每个核心技能一行，线段=生效区间，圆点=变更事件 -->
-            <div class="evo-chart-title text-aux text-secondary">能力生命线（近 8 季度 · 按当前需求指数取 Top 12；线段 = 生效区间，圆点 = 变更事件）</div>
-            <div class="evo-chart-desc">怎么看：每行是一项核心技能的"在岗生命周期"——横线表示这段时间里该技能属于岗位要求，彩色圆点标记发生变化的时间点（颜色含义见图例）。一眼看出技能何时进入、何时增强、何时退出。</div>
-            <div class="evo-gantt" id="evo-gantt"></div>
-
             <!-- 能力更替流：旧核心 → 持续能力 → 新核心 -->
             <div class="evo-chart-title text-aux text-secondary">能力更替流（旧核心 → 持续能力 → 新核心）</div>
             <div class="evo-chart-desc">是什么：把岗位技能按演化结果分三列——左边是已退出的旧要求，中间是一直有效的持续能力，右边是新进入的要求。用于回答"这个岗位正在从什么技术栈转向什么技术栈"。</div>

webui/style.css

@@ -1168,3 +1168,15 @@ svg { width: 16px; height: 16px; stroke: currentColor; fill: none; stroke-width:
 
 /* 演化页图表说明文字 */
 .evo-chart-desc { font-size: 12px; color: var(--text-3); line-height: 1.7; margin: -2px 0 8px; }
+
+/* 全岗位扫描：横条排行 */
+.evo-scan { margin-bottom: 16px; }
+.scan-row {
+  display: flex; align-items: center; gap: 10px; padding: 5px 4px;
+  cursor: pointer; border-radius: var(--radius-sm);
+}
+.scan-row:hover { background: var(--bg-hover); }
+.scan-label { flex: none; width: 132px; font-size: 13px; color: var(--text-1); text-align: right; }
+.scan-track { flex: 1; display: flex; height: 14px; background: var(--bg-page); border-radius: 3px; overflow: hidden; }
+.scan-bar { display: block; height: 100%; }
+.scan-num { flex: none; width: 34px; font-size: 12px; color: var(--text-2); font-variant-numeric: tabular-nums; }