Compare commits
4
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 RoyirinRosmontis
|
3aa8f1ceab | ||
|
RoyirinRosmontis
|
07bd9e12fd | ||
|
RoyirinRosmontis
|
ef9fe9faa3 | ||
|
RoyirinRosmontis
|
d2be567668 |
@@ -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
|
||||
|
||||
+20
-3
@@ -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)],
|
||||
required_skills=[s for s, c in req.most_common() if c >= n * 0.4],
|
||||
industries=industries,
|
||||
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,
|
||||
|
||||
+28
-28
@@ -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
|
||||
},
|
||||
{
|
||||
|
||||
+108
-13
@@ -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
|
||||
rec, _ = make_jd(a, year, f"J{seq:05d}", month=i * 12 // count + 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；
|
||||
# 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,
|
||||
})
|
||||
|
||||
+2265
-2329
File diff suppressed because it is too large
Load Diff
+4
-9
@@ -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 -->
|
||||
<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-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-heatmap" id="evo-heatmap"><div class="empty small">加载中…</div></div>
|
||||
<!-- 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，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-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>
|
||||
|
||||
@@ -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; }
|
||||
|
||||
Reference in New Issue
Block a user