7409fabc58
=== 컬럼명칭 통일 (c{prefix} → C-{prefix}11) ===
Python 분석스크립트: data pkl 경로 →
gen_temp_profiles: tempref 파일명 →
SteamAdvisorController: TagsFor() 숫자서픽스 → 풀컬럼키(C-6111), ToSuffix() 변환
steam.js: ST_TEMP_COLS ['61',...] → ['C-6111',...], selectbox defaultColumn
appsettings.json: Columns 키 c61/c62/... → C-6111/C-6211/..., DefaultColumn c6111→C-6111
run_column.py: 추출/분석시 col_key = f"C-{{prefix}}11"
C-{x}11_{model,tempref}.json: 신규 명칭 기준 기준프로파일/모델 7컬럼분
=== SteamAdvisor 수정 ===
SteamModel: [JsonPropertyName] 매핑(snake_case → PascalCase 역직렬화)
예외처리: LinearCoeffs.Count < 3 방어코드
steam.js: catch(_) {} → 에러메시지 표시, missing_tags 응답처리
=== Feedforward Controller 개선 ===
ff.js: 상승/하강 양방향 램프 confirm, 방향뱃지(↑↓), Normal 모드 표시
FeedforwardController: 업램프 단독제한 제거(양방향), tcReturnTcTarget/Band 노출
=== DB ===
Hc900DbContext: realtime_table_tagname_key 레거시 UNIQUE 제약/인덱스 DROP 로직
Hc900Controllers: ToDictionaryAsync → GroupBy 변환 (중복 tagname 대응)
=== SIGPIPE 대응 ===
gateway.cpp: signal(SIGPIPE, SIG_IGN) 메인스레드 설치
modbus_tcp.cpp: send() flags 0 → MSG_NOSIGNAL (EPIPE 복구)
sigpipe_ignore.c: LD_PRELOAD 우회 공유라이브러리
Hc900GatewayProcessService: LD_PRELOAD 환경변수 설정
101 lines
4.4 KiB
Python
101 lines
4.4 KiB
Python
"""
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Shadow 예측기 — 히스토리 리플레이 백테스트.
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선행: c6111_data.pkl. 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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from sklearn.ensemble import GradientBoostingRegressor
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from sklearn.metrics import r2_score, mean_absolute_error
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BASE = "/home/windpacer/projects/hc900_ax/scripts/analysis/"
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FEATURES = ["feed", "product", "T_C"]
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SMOOTH = 40
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TRAIN_FRAC = 0.70
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class SteamPredictor:
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"""운전점 학습 + 밸브 역특성(flow→OP)."""
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def fit(self, df_train):
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ops = (df_train.set_index("dtat").resample("6h").median(numeric_only=True)
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.dropna(subset=["steam_flow", "feed"]))
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ops = ops[ops["feed"] > 50]
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self.model = GradientBoostingRegressor(n_estimators=200, max_depth=2,
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learning_rate=0.05, random_state=0)
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self.model.fit(ops[FEATURES].values, ops["steam_flow"].values)
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# 밸브 역특성: OP = poly(flow) (단조, 3차)
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self.inv = np.polyfit(df_train["steam_flow"], df_train["steam_op"], 3)
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return self
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def predict_flow(self, X):
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return self.model.predict(X)
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def flow_to_op(self, flow):
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return np.clip(np.polyval(self.inv, flow), 0, 100)
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--data", default=BASE + "C-6111_data.pkl")
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parser.add_argument("--prefix", default="C-6111")
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args = parser.parse_args()
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df = pd.read_pickle(args.data)
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df = df[df["mode"] == "PROD"].copy()
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df = df[(df["feed"] > 50) & (df["steam_flow"] > 10) & (df["steam_op"] > 1)
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& df[FEATURES + ["steam_op"]].notna().all(axis=1)].sort_values("dtat")
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# 입력 평활 (실제 shadow도 노이즈 평활 사용)
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for c in FEATURES:
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df[c + "_s"] = df[c].rolling(SMOOTH, min_periods=1, center=True).median()
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cut = df["dtat"].quantile(TRAIN_FRAC)
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tr, te = df[df["dtat"] <= cut], df[df["dtat"] > cut]
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print(f"학습 {tr.dtat.min()}~{tr.dtat.max()} ({len(tr)}) "
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f"shadow(held-out) {te.dtat.min()}~{te.dtat.max()} ({len(te)})")
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pred = SteamPredictor().fit(tr)
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# OOD(학습 운전envelope 밖) 게이트: 입력이 학습 1~99% 범위 밖이면 '저신뢰→오퍼레이터 폴백'
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lo, hi = tr[FEATURES].quantile(0.01), tr[FEATURES].quantile(0.99)
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print(f"학습 envelope: " + ", ".join(f"{c}[{lo[c]:.0f},{hi[c]:.1f}]" for c in FEATURES))
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def in_env(d):
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return ((d[FEATURES] >= lo) & (d[FEATURES] <= hi)).all(axis=1)
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for name, d in [("학습기간", tr), ("★held-out shadow", te)]:
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Xs = d[[c + "_s" for c in FEATURES]].values
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pf = pred.predict_flow(Xs)
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po = pred.flow_to_op(pf)
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ao = d["steam_op"].values
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env = in_env(d).values
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within = np.mean(np.abs(po - ao) <= 2.0) * 100
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print(f"\n[{name}] OOD(범위밖)={100*(~env).mean():.1f}%")
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print(f" 전체 OP MAE={mean_absolute_error(ao,po):.2f} |Δ|≤2%={within:.1f}%")
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if env.sum() > 50:
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print(f" in-envelope OP MAE={mean_absolute_error(ao[env],po[env]):.2f} "
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f"|Δ|≤2%={np.mean(np.abs(po[env]-ao[env])<=2)*100:.1f}% ← shadow가 신뢰구간에서 조언")
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d = d.assign(pred_flow=pf, pred_op=po, ood=~env)
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if name.startswith("★"):
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te = d
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# 플롯: held-out 시계열 오버레이 + OP 비교 + 오차분포
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fig, ax = plt.subplots(3, 1, figsize=(16, 11))
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s = te.iloc[::20]
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ax[0].plot(s.dtat, s["steam_flow"], lw=.6, label="actual steam flow")
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ax[0].plot(s.dtat, s["pred_flow"], lw=.6, c="r", label="predicted")
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ax[0].set_title("held-out shadow: steam flow actual vs predicted"); ax[0].legend(fontsize=8)
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ax[1].plot(s.dtat, s["steam_op"], lw=.6, label="actual operator OP")
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ax[1].plot(s.dtat, s["pred_op"], lw=.6, c="r", label="predicted OP")
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ax[1].set_ylabel("OP %"); ax[1].set_title("operator OP vs shadow-predicted OP"); ax[1].legend(fontsize=8)
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err = te["pred_op"] - te["steam_op"]
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ax[2].hist(err, bins=80); ax[2].axvline(0, c="k", lw=.5)
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ax[2].set_title(f"OP error (pred-actual): median {err.median():+.2f}%, std {err.std():.2f}%")
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fig.tight_layout(); fig.savefig(BASE + f"{args.prefix}_shadow.png", dpi=95)
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print(f"\n플롯 저장: {BASE}{args.prefix}_shadow.png")
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if __name__ == "__main__":
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main()
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