4306f76ddb
- c6111_extract: roles_for() 동적 생성, COLUMN_EXCEPTIONS per-prefix - c6111_prodmap/shadow/startup/rolling: --data/--prefix CLI 인자 지원 - run_column.py: 5개 컬럼 전 파이프라인 실행 래퍼 - c6111_shutdown.py: detect_cutoffs + shutdown_milestones (lookback 1200) - c6111_operator_assist.py: OOD 게이트 + shadow 리플레이 - c6111_export_model.py: 선형근사 JSON export - SteamAdvisor.cs: Predict+ClassifyMode+InEnvelope (NaN guard, Ood fix) - SteamAdvisorController: GET/POST /api/steam/predict - appsettings.json/Program.cs: DI 등록 - docs: 작업지시서 현황 갱신, 진단보고서 작성 (3 MED/8 LOW, 100% 정확도)
112 lines
4.6 KiB
Python
112 lines
4.6 KiB
Python
"""
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② START-UP 절차 학습 (few-shot).
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형제 컬럼 호환: --data, --prefix CLI 인자.
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"""
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import argparse
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import numpy as np
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import pandas as pd
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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BASE = "/home/windpacer/projects/hc900_ax/scripts/analysis/"
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def detect_cutins(df):
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"""★제품 컷인★ 이벤트: product 0→>100 상향, 직전 30분 라인아웃(product<50)이고 hot(reb>75)."""
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prod = df["product"].values
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reb = df["reb_temp"].values
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outs = []
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i = 60
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n = len(df)
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while i < n:
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if prod[i] > 100 and prod[i-1] <= 100:
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pre = prod[max(0, i-60):i] # 직전 30분
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if np.nanmedian(pre) < 50 and reb[i] > 75: # 라인아웃(제품off)+hot
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outs.append(i)
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i += 720
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continue
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i += 1
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return outs
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def milestones(df, ci):
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"""제품 컷인 인덱스 ci 기준 절차 추출."""
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tc = df["dtat"].iloc[ci]
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# 역방향: 스팀투입(steam_op>10 연속 시작) — 컷인 직전 steam off→on
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back = df.iloc[max(0, ci-1200):ci]
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off = back[back["steam_op"] <= 10]
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i_steam = off.index[-1] + 1 if len(off) else back.index[0]
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# 리플럭스 확립(스팀투입 이후 reflux>100 첫)
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aft = df.iloc[i_steam:ci]
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r_on = aft[aft["reflux"] > 100]
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i_refl = r_on.index[0] if len(r_on) else None
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# 풀로드(컷인 이후 feed>250 첫)
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fwd = df.iloc[ci:ci+1200]
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f_on = fwd[fwd["feed"] > 250]
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i_full = f_on.index[0] if len(f_on) else None
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def mins(i):
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return None if i is None else (df["dtat"].iloc[i]-tc).total_seconds()/60
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r = df.iloc[ci]
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return dict(cutin_time=tc,
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steam_to_cutin=-mins(i_steam),
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reflux_to_cutin=(-mins(i_refl) if i_refl is not None else None),
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cutin_to_full=mins(i_full),
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cutin_rebA=r["reb_temp"], cutin_TC=r["T_C"], cutin_TD=r["T_D"],
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cutin_dT_AD=r["reb_temp"]-r["T_D"])
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--data", default=BASE + "c6111_data.pkl")
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parser.add_argument("--prefix", default="c6111")
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args = parser.parse_args()
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df = pd.read_pickle(args.data).sort_values("dtat").reset_index(drop=True)
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cutins = detect_cutins(df)
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print(f"탐지된 ★제품 컷인★(진짜 startup) 이벤트: {len(cutins)}개")
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rows, windows = [], []
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for ci in cutins:
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w = df.iloc[max(0, ci-360):min(len(df), ci+360)].copy() # 컷인 ±3h
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w["rel_min"] = (w["dtat"] - df["dtat"].iloc[ci]).dt.total_seconds()/60
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windows.append(w)
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rows.append(milestones(df, ci))
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M = pd.DataFrame(rows)
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pd.set_option("display.width", 220)
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print("\n=== 제품컷인 기준 절차(분) + 컷인 시점 컬럼상태 ===")
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cols = ["cutin_time", "steam_to_cutin", "reflux_to_cutin", "cutin_to_full",
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"cutin_rebA", "cutin_TC", "cutin_dT_AD"]
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show = M[cols].copy()
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show["cutin_time"] = show["cutin_time"].dt.strftime("%m-%d %H:%M")
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print(show.round(1).to_string(index=False))
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print("\n=== 절차 레시피(중앙값) ===")
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print(f" 스팀투입→제품컷인(전환류 라인아웃 길이): {M.steam_to_cutin.median():.0f}분")
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print(f" 리플럭스확립→제품컷인 : {M.reflux_to_cutin.median():.0f}분")
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print(f" 제품컷인→풀로드 : {M.cutin_to_full.median():.0f}분")
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print(f" ★제품컷인 트리거(컬럼상태): reb-A={M.cutin_rebA.median():.1f}±{M.cutin_rebA.std():.1f}℃, "
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f"T_C={M.cutin_TC.median():.1f}±{M.cutin_TC.std():.2f}℃, ΔT(A-D)={M.cutin_dT_AD.median():.1f}℃")
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fig, ax = plt.subplots(4, 1, figsize=(13, 11), sharex=True)
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for k, w in enumerate(windows):
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c = plt.cm.tab10(k)
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ax[0].plot(w.rel_min, w.reb_temp, color=c, lw=.9, label=f"ep{k+1} {w.dtat.iloc[len(w)//2]:%m-%d}")
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ax[0].plot(w.rel_min, w["T_D"], color=c, lw=.6, ls=":")
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ax[1].plot(w.rel_min, w.steam_flow, color=c, lw=.9)
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ax[2].plot(w.rel_min, w.reflux, color=c, lw=.9)
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ax[2].plot(w.rel_min, w["product"], color=c, lw=.9, ls="--")
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ax[3].plot(w.rel_min, w.feed, color=c, lw=.9)
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ax[0].set_ylabel("reb_temp/T_D(:)"); ax[0].legend(fontsize=7)
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ax[0].set_title("STARTUP aligned at PRODUCT CUT-IN (rel=0)")
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ax[1].set_ylabel("steam flow"); ax[2].set_ylabel("reflux/product(--)")
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ax[3].set_ylabel("feed"); ax[3].set_xlabel("minutes from product cut-in")
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for a in ax:
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a.axvline(0, c="k", lw=.5)
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fig.tight_layout(); fig.savefig(BASE + f"{args.prefix}_startup.png", dpi=95)
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print(f"\n플롯 저장: {BASE}{args.prefix}_startup.png")
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if __name__ == "__main__":
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main()
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