import json
import os
import pickle
import random
import re
import threading
import time
from collections import Counter, defaultdict
from dataclasses import dataclass
from datetime import date, datetime, time as dt_time, timedelta
from typing import Any, Dict, List, Optional, Set, Tuple

import pandas as pd
import requests
import urllib3
from dotenv import load_dotenv

from schedule_api_client import clean_movie_title, fetch_hall_info, fetch_schedule_data, get_valid_token


urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
load_dotenv()



CONFIG_FILE = os.path.join("cinema_cache", "nextday_schedule_optimizer_config.json")
JOB_STATE_FILE = os.path.join("cinema_cache", "nextday_optimizer_job_state.json")
JOB_PAYLOAD_FILE = os.path.join("cinema_cache", "nextday_optimizer_job_payload.pkl")
JOB_RESULT_FILE = os.path.join("cinema_cache", "nextday_optimizer_job_result.pkl")

_JOB_THREAD: Optional[threading.Thread] = None

DEFAULT_CONFIG: Dict[str, Any] = {
    "business_start": "09:30",
    "business_end": "01:30",
    "turnaround_base": 10,
    "golden_start": "14:00",
    "golden_end": "21:00",
    "efficiency_enabled": True,
    "efficiency_penalty_coef": 1.0,
    "eff_daily_delta_cap": 5,
    "rule1_enabled": True,
    "rule1_gap": 30,
    "rule2_enabled": True,
    "rule2_threshold": 4,
    "rule2_window_minutes": 30,
    "rule2_penalty": 15.0,
    "rule2_exempt_ranges": ["14:00-15:00", "19:00-20:00"],
    "rule3_enabled": True,
    "rule3_gap_minutes": 30,
    "rule3_penalty": 12.0,
    "rule4_enabled": True,
    "rule4_earliest": "10:00",
    "rule4_latest": "22:30",
    "rule9_enabled": True,
    "rule9_hot_top_n": 3,
    "rule9_min_ratio": 0.30,
    "rule9_penalty": 20.0,
    "rule11_enabled": True,
    "rule11_after_time": "22:00",
    "rule11_penalty": 30.0,
    "rule12_enabled": True,
    "rule12_penalty_each": 25.0,
    "rule13_enabled": True,
    "rule13_forbidden_halls": ["2", "8", "9"],
    "tms_allowance": 0,
    "maintenance_blocks": [],
    "iterations": 300,
    "random_seed": 20260331,
}


@dataclass
class RuleContext:
    target_date: date
    business_start_dt: datetime
    business_end_dt: datetime
    golden_start_dt: datetime
    golden_end_dt: datetime
    params: Dict[str, Any]
    blockouts_by_hall: Dict[str, List[Tuple[datetime, datetime]]]
    movie_targets: Dict[str, Dict[str, Any]]
    movie_weights: Dict[str, float]
    tms_by_hall: Dict[str, List[Dict[str, Any]]]
    manual_constraints: Dict[str, Dict[str, Optional[float]]]
    allowed_movies: Set[str]
    preview_windows_by_identity: Dict[str, List[Tuple[datetime, datetime]]]


@dataclass
class CandidateResult:
    schedule: List[Dict[str, Any]]
    score: float
    score_breakdown: List[Tuple[str, float, str]]
    hard_violations: List[str]


def serialize_candidate(cand: CandidateResult) -> Dict[str, Any]:
    return {
        "schedule": cand.schedule,
        "score": float(cand.score),
        "score_breakdown": [list(x) for x in (cand.score_breakdown or [])],
        "hard_violations": list(cand.hard_violations or []),
    }


def deserialize_candidate(obj: Any) -> Optional[CandidateResult]:
    if isinstance(obj, CandidateResult):
        return obj
    if not isinstance(obj, dict):
        return None
    score_breakdown = obj.get("score_breakdown") or []
    parsed_bd: List[Tuple[str, float, str]] = []
    for x in score_breakdown:
        if isinstance(x, (list, tuple)) and len(x) >= 3:
            parsed_bd.append((str(x[0]), float(x[1]), str(x[2])))
    return CandidateResult(
        schedule=list(obj.get("schedule") or []),
        score=float(obj.get("score") or 0.0),
        score_breakdown=parsed_bd,
        hard_violations=list(obj.get("hard_violations") or []),
    )


def ensure_cache_dir() -> None:
    os.makedirs(os.path.dirname(CONFIG_FILE), exist_ok=True)


def load_config() -> Dict[str, Any]:
    ensure_cache_dir()
    if not os.path.exists(CONFIG_FILE):
        return dict(DEFAULT_CONFIG)
    try:
        with open(CONFIG_FILE, "r", encoding="utf-8") as f:
            loaded = json.load(f)
        cfg = dict(DEFAULT_CONFIG)
        cfg.update(loaded)
        return cfg
    except Exception:
        return dict(DEFAULT_CONFIG)


def save_config(cfg: Dict[str, Any]) -> None:
    ensure_cache_dir()
    with open(CONFIG_FILE, "w", encoding="utf-8") as f:
        json.dump(cfg, f, ensure_ascii=False, indent=2)


def _atomic_write_json(path: str, payload: Dict[str, Any]) -> None:
    ensure_cache_dir()
    tmp = f"{path}.tmp"
    with open(tmp, "w", encoding="utf-8") as f:
        json.dump(payload, f, ensure_ascii=False, indent=2)
    os.replace(tmp, path)


def _read_json(path: str, default: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
    if default is None:
        default = {}
    if not os.path.exists(path):
        return dict(default)
    try:
        with open(path, "r", encoding="utf-8") as f:
            data = json.load(f)
        if isinstance(data, dict):
            out = dict(default)
            out.update(data)
            return out
        return dict(default)
    except Exception:
        return dict(default)


def _now_text() -> str:
    return datetime.now().strftime("%Y-%m-%d %H:%M:%S")


def _default_job_state() -> Dict[str, Any]:
    return {
        "status": "idle",
        "control": "run",   # run | pause | stop
        "job_id": "",
        "started_at": "",
        "started_ts": 0.0,
        "ended_at": "",
        "updated_at": "",
        "target_date": "",
        "iterations": 0,
        "iter_done": 0,
        "progress": 0.0,
        "elapsed_seconds": 0.0,
        "feasible_count": 0,
        "hard_reject": 0,
        "build_reject": 0,
        "rule_reject": 0,
        "reject_reason_top": {},
        "reject_detail_top": {},
        "message": "",
        "result_count": 0,
    }


def _atomic_write_pickle(path: str, payload: Any) -> None:
    ensure_cache_dir()
    tmp = f"{path}.tmp"
    with open(tmp, "wb") as f:
        pickle.dump(payload, f)
    os.replace(tmp, path)


def _read_pickle(path: str, default: Any = None) -> Any:
    if not os.path.exists(path):
        return default
    try:
        with open(path, "rb") as f:
            return pickle.load(f)
    except Exception:
        return default


def _find_live_worker() -> Optional[threading.Thread]:
    global _JOB_THREAD
    if _JOB_THREAD is not None and _JOB_THREAD.is_alive():
        return _JOB_THREAD
    for t in threading.enumerate():
        if t.name == "nextday-opt-worker" and t.is_alive():
            _JOB_THREAD = t
            return t
    _JOB_THREAD = None
    return None


def read_job_state() -> Dict[str, Any]:
    return _read_json(JOB_STATE_FILE, _default_job_state())


def write_job_state(**kwargs: Any) -> Dict[str, Any]:
    state = read_job_state()
    state.update(kwargs)
    state["updated_at"] = _now_text()
    _atomic_write_json(JOB_STATE_FILE, state)
    return state


def parse_hm(hm: str, fallback: str) -> dt_time:
    raw = str(hm or "").strip()
    if not raw:
        raw = fallback
    try:
        return datetime.strptime(raw, "%H:%M").time()
    except Exception:
        return datetime.strptime(fallback, "%H:%M").time()


def hm_str(t: dt_time) -> str:
    return t.strftime("%H:%M")


def parse_operating_dt(d: date, t: dt_time) -> datetime:
    dt = datetime.combine(d, t)
    if t < dt_time(6, 0):
        dt += timedelta(days=1)
    return dt


def ceil_datetime_to_step(dt: datetime, step_minutes: int = 5) -> datetime:
    aligned = dt.replace(second=0, microsecond=0)
    if aligned.minute % step_minutes == 0 and dt.second == 0 and dt.microsecond == 0:
        return aligned
    add_minutes = (step_minutes - (aligned.minute % step_minutes)) % step_minutes
    if add_minutes == 0:
        add_minutes = step_minutes
    return aligned + timedelta(minutes=add_minutes)


def normalize_hall_key(hall_id: Any, hall_name: Any) -> str:
    if hall_id not in (None, ""):
        return str(hall_id)
    if hall_name in (None, ""):
        return ""
    nums = re.findall(r"\d+", str(hall_name))
    return nums[0] if nums else str(hall_name)


def extract_hall_no(raw: Any) -> str:
    nums = re.findall(r"\d+", str(raw or ""))
    return nums[0] if nums else str(raw or "")


def normalize_media_type(media: Any) -> str:
    text = str(media or "").upper()
    if "3D" in text:
        return "3D"
    if "2D" in text:
        return "2D"
    return ""


def movie_policy_key(movie_name: Any, movie_media_type: Any = "") -> str:
    """
    片名策略键：
    - 同片不同语言归并（依赖 clean_movie_title 规则）
    - 2D/3D 分开（若 clean 后未体现 3D，则追加）
    """
    base = clean_movie_title(movie_name or "")
    media = normalize_media_type(movie_media_type or movie_name)
    if media == "3D" and "3D" not in str(base).upper():
        return f"{base}（数字3D）"
    return str(base)


def tms_missing_pair_key(session: Dict[str, Any]) -> Tuple[str, str, str]:
    hall_no = extract_hall_no(session.get("hallName") or session.get("hallId"))
    policy = movie_policy_key(session.get("movieName", ""), session.get("movieMediaType", ""))
    media = normalize_media_type(session.get("movieMediaType", ""))
    return hall_no, policy, media


def extract_allowed_movies_from_tuning_df(df: pd.DataFrame) -> Set[str]:
    if df is None or df.empty:
        return set()
    out: Set[str] = set()
    for _, row in df.iterrows():
        selected = row.get("选中", False)
        if pd.notna(selected) and bool(selected):
            key = movie_policy_key(row.get("影片", ""))
            if key:
                out.add(key)
    return out


def normalize_text_token(text: Any) -> str:
    s = str(text or "")
    s = clean_movie_title(s)
    s = re.sub(r"\s+", "", s)
    s = re.sub(r"[\[\]【】()（）·,.，:：!！?？'\"-]", "", s)
    return s.upper()


def to_float(v: Any, default: float = 0.0) -> float:
    try:
        if v in (None, "", "None"):
            return default
        return float(v)
    except Exception:
        return default


def extract_movie_serial_5_8(movie_num: Any) -> str:
    movie_num_norm = re.sub(r"[^A-Z0-9]", "", str(movie_num or "").upper())
    if len(movie_num_norm) >= 8:
        return movie_num_norm[4:8]
    return ""


def movie_identity_key(movie_num: Any, movie_name: Any) -> str:
    serial = extract_movie_serial_5_8(movie_num)
    if serial:
        return f"serial:{serial}"
    return f"name:{clean_movie_title(movie_name or '')}"


def is_3d_by_movie_num_or_media(movie_num: Any, media: Any) -> bool:
    movie_num_norm = re.sub(r"[^A-Z0-9]", "", str(movie_num or "").upper())
    if len(movie_num_norm) >= 4 and movie_num_norm[3] == "2":
        return True
    return "3D" in str(media or "").upper()


def extract_box_office_value(item: Dict[str, Any]) -> float:
    for key in (
        "ticketIncome",
        "splitTicketIncome",
        "todayTicketIncome",
        "todayBoxOffice",
        "boxOffice",
        "box",
        "income",
        "今日票房",
        "今日票房(不含费)",
    ):
        if key not in item:
            continue
        raw = item.get(key)
        if isinstance(raw, str):
            raw = raw.replace(",", "").strip()
        try:
            val = float(raw)
            if val >= 0:
                return val
        except Exception:
            continue
    return 0.0


def sort_movies_by_box_office(box_office_data: List[Dict[str, Any]]) -> List[Tuple[str, float]]:
    score_map: Dict[str, float] = {}
    order_map: Dict[str, int] = {}

    for idx, item in enumerate(box_office_data):
        name = clean_movie_title(item.get("movieName") or item.get("影片名称") or "")
        if not name:
            continue
        val = extract_box_office_value(item)
        if name not in order_map:
            order_map[name] = idx
        score_map[name] = max(score_map.get(name, 0.0), val)

    if not score_map:
        return []

    if max(score_map.values()) > 0:
        ranked = sorted(score_map.items(), key=lambda x: x[1], reverse=True)
    else:
        ranked = sorted(score_map.items(), key=lambda x: order_map.get(x[0], 99999))
    return ranked


def resolve_hot_movies(
    df: pd.DataFrame,
    box_office_data: List[Dict[str, Any]],
    top_n: int,
) -> Tuple[List[str], str, List[Tuple[str, float]]]:
    bo_ranked = sort_movies_by_box_office(box_office_data)
    if bo_ranked:
        top_val = bo_ranked[0][1]
        if top_val > 0:
            hot = [m for m, v in bo_ranked if v >= top_val * 0.95]
        else:
            hot = [m for m, _ in bo_ranked[:top_n]]
        if not hot:
            hot = [m for m, _ in bo_ranked[:top_n]]
        return hot[: max(top_n, len(hot))], "全国大盘票房", bo_ranked

    counts = df["movieClean"].value_counts()
    if counts.empty:
        return [], "无可用数据", []
    max_count = int(counts.iloc[0])
    hot = counts[counts >= max_count * 0.95].index.tolist()
    if not hot:
        hot = counts.head(top_n).index.tolist()
    fallback_ranked = [(m, float(c)) for m, c in counts.items()]
    return hot[: max(top_n, len(hot))], "场次数量", fallback_ranked


def rule9_core_windows(d: date) -> List[Tuple[dt_time, dt_time]]:
    weekday = d.weekday()
    windows = [
        [(dt_time(14, 0), dt_time(16, 0)), (dt_time(19, 0), dt_time(22, 0))],
        [(dt_time(14, 0), dt_time(15, 30)), (dt_time(19, 0), dt_time(22, 20))],
        [(dt_time(14, 30), dt_time(16, 0)), (dt_time(19, 0), dt_time(21, 40))],
        [(dt_time(14, 0), dt_time(16, 0)), (dt_time(19, 0), dt_time(22, 0))],
        [(dt_time(14, 0), dt_time(15, 0)), (dt_time(19, 0), dt_time(22, 0))],
        [(dt_time(14, 0), dt_time(16, 0)), (dt_time(19, 0), dt_time(22, 0))],
        [(dt_time(14, 0), dt_time(17, 0)), (dt_time(19, 0), dt_time(21, 30))],
    ]
    return windows[weekday]


def time_in_ranges(t: dt_time, ranges: List[Tuple[dt_time, dt_time]]) -> bool:
    for st_t, et_t in ranges:
        if st_t <= et_t:
            if st_t <= t < et_t:
                return True
        else:
            if t >= st_t or t < et_t:
                return True
    return False


def gap_intersects_any_blockout(
    g_st: datetime,
    g_et: datetime,
    blockouts_by_hall: Dict[str, List[Tuple[datetime, datetime]]],
) -> bool:
    for _, ranges in blockouts_by_hall.items():
        for b_st, b_et in ranges:
            if interval_overlaps(g_st, g_et, b_st, b_et):
                return True
    return False


def parse_exempt_ranges(items: List[str]) -> List[Tuple[dt_time, dt_time]]:
    out: List[Tuple[dt_time, dt_time]] = []
    for item in items:
        s = str(item or "").strip()
        if not s:
            continue
        if "-" not in s:
            continue
        try:
            st_s, et_s = s.split("-", 1)
            out.append((datetime.strptime(st_s.strip(), "%H:%M").time(), datetime.strptime(et_s.strip(), "%H:%M").time()))
        except Exception:
            continue
    return out


def in_any_exempt(ts: datetime, ranges: List[Tuple[dt_time, dt_time]]) -> bool:
    t = ts.time()
    for st_t, et_t in ranges:
        if st_t <= et_t:
            if st_t <= t <= et_t:
                return True
        else:
            if t >= st_t or t <= et_t:
                return True
    return False


def interval_overlaps(a_st: datetime, a_et: datetime, b_st: datetime, b_et: datetime) -> bool:
    return not (a_et <= b_st or a_st >= b_et)


def gap_intersects_blockout(
    hall_key: str,
    g_st: datetime,
    g_et: datetime,
    blockouts_by_hall: Dict[str, List[Tuple[datetime, datetime]]],
) -> bool:
    for b_st, b_et in blockouts_by_hall.get(hall_key, []):
        if interval_overlaps(g_st, g_et, b_st, b_et):
            return True
    return False


def parse_blockouts_from_config(target_date: date, raw: Any) -> List[Dict[str, Any]]:
    if raw in (None, "", []):
        return []

    parsed: List[Dict[str, Any]]
    if isinstance(raw, str):
        try:
            payload = json.loads(raw)
            parsed = payload if isinstance(payload, list) else []
        except Exception:
            parsed = []
    elif isinstance(raw, list):
        parsed = raw
    else:
        parsed = []

    result: List[Dict[str, Any]] = []
    for item in parsed:
        if not isinstance(item, dict):
            continue
        hall_token = str(item.get("hall") or item.get("hallId") or item.get("hallName") or "").strip()
        st_s = str(item.get("start") or "").strip()
        et_s = str(item.get("end") or "").strip()
        if not hall_token or not st_s or not et_s:
            continue
        try:
            st_t = datetime.strptime(st_s, "%H:%M").time()
            et_t = datetime.strptime(et_s, "%H:%M").time()
            st_dt = parse_operating_dt(target_date, st_t)
            et_dt = parse_operating_dt(target_date, et_t)
            if et_dt <= st_dt:
                et_dt += timedelta(days=1)
            result.append(
                {
                    "hall_token": hall_token,
                    "start": st_dt,
                    "end": et_dt,
                }
            )
        except Exception:
            continue
    return result


def build_hall_blockouts(
    blockouts: List[Dict[str, Any]],
    hall_name_map: Dict[Any, str],
) -> Dict[str, List[Tuple[datetime, datetime]]]:
    out: Dict[str, List[Tuple[datetime, datetime]]] = {str(hid): [] for hid in hall_name_map.keys()}

    for hid, hname in hall_name_map.items():
        hall_key = str(hid)
        hall_no = extract_hall_no(hname)
        for b in blockouts:
            token = str(b["hall_token"])
            token_no = extract_hall_no(token)
            if token in (hall_key, str(hname), hall_no, f"{hall_no}号厅") or token_no == hall_no:
                out.setdefault(hall_key, []).append((b["start"], b["end"]))

    for hall_key in out:
        out[hall_key].sort(key=lambda x: x[0])
    return out


def is_3d_movie(movie: Dict[str, Any]) -> bool:
    text = f"{movie.get('movieMediaType', '')} {movie.get('movieName', '')}".upper()
    return "3D" in text


def fetch_movie_info_for_date(show_date: str) -> List[Dict[str, Any]]:
    token = get_valid_token(force_refresh=False)
    if not token:
        return []

    def _call(tok: str) -> Tuple[int, Dict[str, Any]]:
        url = "https://cawapi.yinghezhong.com/show/getMovieInfo"
        params = {"showDate": show_date, "token": tok, "_": int(time.time() * 1000)}
        headers = {
            "Origin": "https://caw.yinghezhong.com",
            "Referer": "https://caw.yinghezhong.com/",
            "User-Agent": "Mozilla/5.0",
        }
        resp = requests.get(url, params=params, headers=headers, timeout=15)
        resp.raise_for_status()
        payload = resp.json()
        return int(payload.get("code", -1)), payload

    try:
        code, payload = _call(token)
        if code == 1:
            return payload.get("data", []) or []
        if code == 500:
            token = get_valid_token(force_refresh=True)
            if not token:
                return []
            code2, payload2 = _call(token)
            return payload2.get("data", []) if code2 == 1 else []
    except Exception:
        return []
    return []


def dedupe_movies_by_policy_key(movies: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """
    去重规则：
    - 同片不同语言按同一条处理
    - 不同制式（2D/3D）保留
    """
    out: List[Dict[str, Any]] = []
    seen: Set[str] = set()
    for m in movies:
        key = movie_policy_key(m.get("movieName", ""), m.get("movieMediaType", ""))
        if not key or key in seen:
            continue
        seen.add(key)
        out.append(m)
    return out


def build_preview_windows_for_movies(
    target_date: date,
    movies: List[Dict[str, Any]],
) -> Dict[str, List[Tuple[datetime, datetime]]]:
    """
    previewShowTime 规则：
    - previewShowTime 为空：不限时段
    - previewShowTime 有值且命中 target_date：仅允许落在该日对应时段内开场
    - previewShowTime 有值但未命中 target_date：视为该日不限时段
    """
    out: Dict[str, List[Tuple[datetime, datetime]]] = {}
    for m in movies:
        identity = movie_policy_key(m.get("movieName", ""), m.get("movieMediaType", ""))
        if not identity:
            continue
        raw_windows = m.get("previewShowTime") or []
        if not isinstance(raw_windows, list) or not raw_windows:
            continue

        matched_target_date = False
        allowed: List[Tuple[datetime, datetime]] = []
        for w in raw_windows:
            if not isinstance(w, dict):
                continue
            sd_s = str(w.get("startDate") or "").strip()
            ed_s = str(w.get("endDate") or "").strip()
            st_s = str(w.get("startTime") or "").strip()
            et_s = str(w.get("endTime") or "").strip()
            if not sd_s or not ed_s or not st_s or not et_s:
                continue
            try:
                sd = datetime.strptime(sd_s, "%Y-%m-%d").date()
                ed = datetime.strptime(ed_s, "%Y-%m-%d").date()
                if not (sd <= target_date <= ed):
                    continue
                matched_target_date = True
                st_t = datetime.strptime(st_s, "%H:%M").time()
                et_t = datetime.strptime(et_s, "%H:%M").time()
                st_dt = parse_operating_dt(target_date, st_t)
                et_dt = parse_operating_dt(target_date, et_t)
                if et_dt <= st_dt:
                    et_dt += timedelta(days=1)
                allowed.append((st_dt, et_dt))
            except Exception:
                continue

        if matched_target_date and allowed:
            out[identity] = allowed
    return out


def fetch_realtime_box_office(date_str: str) -> List[Dict[str, Any]]:
    token = get_valid_token(force_refresh=False)
    if not token:
        return []
    url = "https://app.bi.piao51.cn/cinema-app/market/realtimeDailyBoxOffice.action"
    params = {"qTime": date_str, "token": token}
    headers = {"Host": "app.bi.piao51.cn", "User-Agent": "Mozilla/5.0"}

    try:
        resp = requests.get(url, params=params, headers=headers, timeout=10)
        resp.raise_for_status()
        data = resp.json()
        if data.get("code") == "A00000":
            return data.get("results", {}).get("movieDatalist", []) or []
    except Exception:
        return []
    return []


def fetch_tms_server_movies_raw() -> List[Dict[str, Any]]:
    app_secret = os.getenv("TMS_APP_SECRET")
    ticket = os.getenv("TMS_TICKET")
    theater_id = int(os.getenv("TMS_THEATER_ID", "0"))
    x_session_id = os.getenv("TMS_X_SESSION_ID")
    if not all([app_secret, ticket, theater_id, x_session_id]):
        return []

    try:
        token_url = f"https://tms.hengdianfilm.com/cinema-api/admin/generateToken?token=hd&murl=ticket={ticket}"
        token_headers = {"Cookie": f"JSESSIONID={x_session_id}", "Content-Type": "application/json"}
        token_payload = {"appId": "hd", "appSecret": app_secret, "timeStamp": int(time.time() * 1000)}
        token_resp = requests.post(token_url, headers=token_headers, json=token_payload, timeout=10)
        token_resp.raise_for_status()
        token_data = token_resp.json()
        auth_token = token_data.get("param")
        if not auth_token:
            return []

        list_url = "https://tms.hengdianfilm.com/cinema-api/cinema/server/dcp/list"
        list_headers = {"Token": auth_token, "X-SESSIONID": x_session_id}
        all_rows: List[Dict[str, Any]] = []
        page_index = 1

        while True:
            payload = {
                "THEATER_ID": theater_id,
                "SOURCE": "SERVER",
                "ASSERT_TYPE": 2,
                "PAGE_CAPACITY": 200,
                "PAGE_INDEX": page_index,
            }
            movie_resp = requests.post(
                list_url,
                params={"token": "hd", "murl": "ContentMovie"},
                headers=list_headers,
                json=payload,
                verify=False,
                timeout=20,
            )
            movie_resp.raise_for_status()
            body = movie_resp.json().get("BODY", {})
            rows = body.get("LIST", []) or []
            if not rows:
                break
            all_rows.extend(rows)
            count = int(body.get("COUNT") or len(all_rows))
            if len(all_rows) >= count:
                break
            page_index += 1
            time.sleep(0.2)

        return all_rows
    except Exception:
        return []


def fetch_schedule_and_halls(show_date: str) -> Tuple[List[Dict[str, Any]], Dict[Any, Any], Optional[str]]:
    token = get_valid_token(force_refresh=False)
    if not token:
        return [], {}, "未获取到有效 token"

    try:
        schedule = fetch_schedule_data(token, show_date)
        halls = fetch_hall_info(token)
        return schedule or [], halls or {}, None
    except ValueError:
        token = get_valid_token(force_refresh=True)
        if not token:
            return [], {}, "token 刷新失败"
        try:
            schedule = fetch_schedule_data(token, show_date)
            halls = fetch_hall_info(token)
            return schedule or [], halls or {}, None
        except Exception as e:
            return [], {}, f"重试后仍失败: {e}"
    except Exception as e:
        return [], {}, str(e)


def build_hall_name_map(next_day_schedule: List[Dict[str, Any]], hall_seat_map: Dict[Any, Any]) -> Dict[Any, str]:
    hall_name_map: Dict[Any, str] = {}
    for s in next_day_schedule:
        hid = s.get("hallId")
        hname = s.get("hallName")
        if hid not in (None, "") and hname:
            hall_name_map[hid] = str(hname)

    if hall_name_map:
        return hall_name_map

    for hid in hall_seat_map.keys():
        hall_name_map[hid] = f"{hid}号厅"

    if not hall_name_map:
        hall_name_map = {1: "1号厅", 2: "2号厅", 3: "3号厅", 4: "4号厅"}
    return hall_name_map


def session_display_label(session: Dict[str, Any]) -> str:
    start = str(session.get("showStartTime") or session.get("startTime") or "").strip()
    hall = str(session.get("hallName") or session.get("hallId") or "").strip()
    movie = str(session.get("movieName") or "").strip()
    return f"{start} | {hall} | {movie}"


def apply_session_exclusions(
    schedule_list: List[Dict[str, Any]],
    excluded_labels: List[str],
) -> List[Dict[str, Any]]:
    if not schedule_list or not excluded_labels:
        return list(schedule_list or [])
    exclude_set = set(str(x).strip() for x in excluded_labels if str(x).strip())
    return [s for s in schedule_list if session_display_label(s) not in exclude_set]


def build_today_efficiency(
    today_schedule: List[Dict[str, Any]],
    hall_seat_map: Dict[Any, Any],
    golden_start: dt_time,
    golden_end: dt_time,
) -> pd.DataFrame:
    if not today_schedule:
        return pd.DataFrame(columns=["影片", "场次", "场次效率", "黄金场次", "黄金效率", "票房"])

    df = pd.DataFrame(today_schedule)
    if df.empty:
        return pd.DataFrame(columns=["影片", "场次", "场次效率", "黄金场次", "黄金效率", "票房"])

    df["影片"] = df.get("movieName", "").apply(clean_movie_title)
    df["总收入"] = pd.to_numeric(df.get("soldBoxOffice", 0), errors="coerce").fillna(0)
    df["放映时间"] = pd.to_datetime(df.get("showStartTime", "00:00"), format="%H:%M", errors="coerce").dt.time

    by_movie = (
        df.groupby("影片", dropna=False)
        .agg(场次=("影片", "size"), 票房=("总收入", "sum"))
        .reset_index()
    )

    total_revenue = float(by_movie["票房"].sum())
    total_sessions = int(by_movie["场次"].sum())
    by_movie["场次效率"] = 0.0

    if total_revenue > 0 and total_sessions > 0:
        by_movie["票房比"] = by_movie["票房"] / total_revenue
        by_movie["场次比"] = by_movie["场次"] / total_sessions
        by_movie["场次效率"] = (
            (by_movie["票房比"] / by_movie["场次比"])
            .replace([float("inf"), -float("inf")], 0)
            .fillna(0)
        )

    golden_df = df[df["放映时间"].between(golden_start, golden_end, inclusive="both")].copy()
    if golden_df.empty:
        by_movie["黄金场次"] = 0
        by_movie["黄金效率"] = 0.0
    else:
        g = (
            golden_df.groupby("影片", dropna=False)
            .agg(黄金场次=("影片", "size"), 黄金票房=("总收入", "sum"))
            .reset_index()
        )
        g_total_revenue = float(g["黄金票房"].sum())
        g_total_count = int(g["黄金场次"].sum())
        g["黄金效率"] = 0.0

        if g_total_revenue > 0 and g_total_count > 0:
            g["黄金票房比"] = g["黄金票房"] / g_total_revenue
            g["黄金场次比"] = g["黄金场次"] / g_total_count
            g["黄金效率"] = (
                (g["黄金票房比"] / g["黄金场次比"])
                .replace([float("inf"), -float("inf")], 0)
                .fillna(0)
            )

        by_movie = by_movie.merge(g[["影片", "黄金场次", "黄金效率"]], on="影片", how="left")
        by_movie["黄金场次"] = by_movie["黄金场次"].fillna(0).astype(int)
        by_movie["黄金效率"] = by_movie["黄金效率"].fillna(0.0)

    return by_movie[["影片", "场次", "场次效率", "黄金场次", "黄金效率", "票房"]]


def build_locked_sessions(raw_next_day_schedule: List[Dict[str, Any]], target_date: date) -> List[Dict[str, Any]]:
    locked: List[Dict[str, Any]] = []
    for s in raw_next_day_schedule:
        sold = int(s.get("soldTicketNum") or s.get("buyTicketNum") or 0)
        if sold <= 0:
            continue
        try:
            st_t = datetime.strptime(str(s.get("showStartTime", "00:00")), "%H:%M").time()
            et_t = datetime.strptime(str(s.get("showEndTime", "00:00")), "%H:%M").time()
        except Exception:
            continue

        st_dt = parse_operating_dt(target_date, st_t)
        et_dt = parse_operating_dt(target_date, et_t)
        if et_dt <= st_dt:
            et_dt += timedelta(days=1)

        locked.append(
            {
                "hallId": s.get("hallId"),
                "hallName": s.get("hallName") or f"{s.get('hallId')}号厅",
                "movieId": s.get("movieId"),
                "movieNum": s.get("movieNum"),
                "movieName": s.get("movieName", "未知影片"),
                "movieDuration": int(s.get("movieLength") or s.get("movieDuration") or 120),
                "movieMediaType": s.get("movieMediaType", ""),
                "startTime": st_dt,
                "endTime": et_dt,
                "is_presold": True,
                "sold": sold,
            }
        )
    return locked


def build_tms_index_by_hall(tms_rows: List[Dict[str, Any]]) -> Dict[str, List[Dict[str, Any]]]:
    by_hall: Dict[str, List[Dict[str, Any]]] = defaultdict(list)

    for row in tms_rows:
        halls = row.get("HALL_INFO") or []
        if not isinstance(halls, list) or not halls:
            continue

        content_name = str(row.get("CONTENT_NAME") or "")
        assert_name = str(row.get("ASSERT_NAME") or "")
        assert_id = str(row.get("ASSERT_ID") or "")
        source_format = str(row.get("SOURCE_FORMAT") or "")

        entry = {
            "assert_12": re.sub(r"[^A-Za-z0-9]", "", assert_id).upper()[:12],
            "name_norm": normalize_text_token(assert_name or content_name),
            "media": normalize_media_type(source_format),
        }

        for hall in halls:
            hall_key = extract_hall_no(hall.get("HALL_NAME") or hall.get("HALL_ID"))
            by_hall[hall_key].append(entry)

    return dict(by_hall)


def session_in_tms(session: Dict[str, Any], hall_key: str, tms_by_hall: Dict[str, List[Dict[str, Any]]]) -> bool:
    if not tms_by_hall:
        return True

    entries = tms_by_hall.get(extract_hall_no(hall_key), [])
    if not entries:
        return False

    movie_name_norm = normalize_text_token(session.get("movieName"))
    movie_num_12 = re.sub(r"[^A-Za-z0-9]", "", str(session.get("movieNum") or "")).upper()[:12]
    media = normalize_media_type(session.get("movieMediaType"))

    for e in entries:
        id_ok = bool(movie_num_12) and movie_num_12 == e.get("assert_12")
        name_norm = e.get("name_norm") or ""
        name_ok = movie_name_norm and (
            movie_name_norm == name_norm
            or (movie_name_norm in name_norm)
            or (name_norm in movie_name_norm)
        )
        media_ok = (not media) or (not e.get("media")) or media == e.get("media")
        if media_ok and (id_ok or name_ok):
            return True

    return False


def build_movie_targets(
    movies: List[Dict[str, Any]],
    today_eff: pd.DataFrame,
    locked_sessions: List[Dict[str, Any]],
    box_office_data: List[Dict[str, Any]],
    rule12_enabled: bool = True,
) -> Dict[str, Dict[str, Any]]:
    locked_total = Counter(movie_policy_key(s.get("movieName", ""), s.get("movieMediaType", "")) for s in locked_sessions)
    locked_golden = Counter()

    targets: Dict[str, Dict[str, Any]] = {}
    eff_map = {
        movie_policy_key(r["影片"]): r
        for _, r in (today_eff.iterrows() if not today_eff.empty else [])
    }

    rank_boost: Dict[str, float] = {}
    bo_ranked = sort_movies_by_box_office(box_office_data)
    top10 = {mv for mv, _ in bo_ranked[:10]}
    top5 = {mv for mv, _ in bo_ranked[:5]} if rule12_enabled else set()
    for i, (mv, _) in enumerate(bo_ranked[:10], start=1):
        rank_boost[mv] = max(0.6, 1.6 - 0.1 * i)

    for m in movies:
        mv = movie_policy_key(m.get("movieName", ""), m.get("movieMediaType", ""))
        if not mv:
            continue

        eff = eff_map.get(mv)
        today_total = int(eff.get("场次", 0)) if eff is not None else 0
        today_golden = int(eff.get("黄金场次", 0)) if eff is not None else 0
        fe = float(eff.get("场次效率", 1.0)) if eff is not None else 1.0
        ge = float(eff.get("黄金效率", 1.0)) if eff is not None else 1.0

        if today_total <= 0:
            if mv in top10:
                min_total, max_total = 0, 1
            else:
                min_total, max_total = 0, 0
            min_golden = 0
        else:
            if fe > 1.5:
                min_total, max_total = today_total + 1, today_total + 4
            elif fe < 0.5:
                min_total, max_total = max(0, today_total - 1), max(today_total, 1)
            else:
                min_total, max_total = max(0, today_total - 1), today_total + 2

            if ge > 1.5:
                min_golden = today_golden + 1
            elif ge < 0.5:
                min_golden = max(0, today_golden - 1)
            else:
                min_golden = max(0, today_golden)

            if today_golden == 0 and fe > 1.5:
                min_golden = max(1, min_golden)

        # 规则十二优先：票房Top5至少给1个黄金场，并保证总场次可容纳
        if mv in top5:
            min_golden = max(1, min_golden)
            min_total = max(min_total, 1)
            max_total = max(max_total, 1)

        lt = int(locked_total.get(mv, 0))
        lg = int(locked_golden.get(mv, 0))

        min_total = max(min_total, lt)
        min_golden = max(min_golden, lg)
        max_total = max(max_total, min_total)

        targets[mv] = {
            "min_total": int(min_total),
            "max_total": int(max_total),
            "min_golden": int(min_golden),
            "today_total": int(today_total),
            "today_golden": int(today_golden),
            "fe": float(fe),
            "ge": float(ge),
            "base_weight": float(rank_boost.get(mv, 1.0)),
        }

    return targets


def build_movie_weights(
    movies: List[Dict[str, Any]],
    movie_targets: Dict[str, Dict[str, Any]],
    box_office_data: List[Dict[str, Any]],
) -> Dict[str, float]:
    weights: Dict[str, float] = {}

    rank_map: Dict[str, int] = {}
    bo_ranked = sort_movies_by_box_office(box_office_data)
    for i, (mv, _) in enumerate(bo_ranked[:20], start=1):
        rank_map[mv] = i

    for m in movies:
        mv = movie_policy_key(m.get("movieName", ""), m.get("movieMediaType", ""))
        if not mv:
            continue

        w = 1.0
        rank = rank_map.get(mv)
        if rank is not None:
            w *= max(0.7, 1.8 - 0.08 * rank)

        target = movie_targets.get(mv, {})
        fe = float(target.get("fe", 1.0) or 1.0)
        ge = float(target.get("ge", 1.0) or 1.0)

        if fe > 1.5:
            w *= 1.2
        elif fe < 0.5:
            w *= 0.85

        if ge > 1.5:
            w *= 1.1
        elif ge < 0.5:
            w *= 0.92

        weights[mv] = max(0.1, w)

    return weights


def can_place(
    session: Dict[str, Any],
    hall_sessions: List[Dict[str, Any]],
    all_sessions: List[Dict[str, Any]],
    turn_min: int,
    turn_max: int,
    hall_key: str,
    ctx: RuleContext,
) -> bool:
    st_dt = session["startTime"]
    et_dt = session["endTime"]
    if et_dt <= st_dt:
        return False

    blockouts = ctx.blockouts_by_hall.get(hall_key, [])
    for b_st, b_et in blockouts:
        if interval_overlaps(st_dt, et_dt, b_st, b_et):
            return False

    same_hall = sorted(hall_sessions, key=lambda x: x["startTime"])
    for s in same_hall:
        if interval_overlaps(st_dt, et_dt, s["startTime"], s["endTime"]):
            return False

    prev_session: Optional[Dict[str, Any]] = None
    next_session: Optional[Dict[str, Any]] = None

    for s in same_hall:
        if s["endTime"] <= st_dt:
            prev_session = s
        elif s["startTime"] >= et_dt:
            next_session = s
            break

    if prev_session is not None:
        gap = (st_dt - prev_session["endTime"]).total_seconds() / 60
        if gap < turn_min:
            return False
        if gap > turn_max and not gap_intersects_blockout(hall_key, prev_session["endTime"], st_dt, ctx.blockouts_by_hall):
            return False

    if next_session is not None:
        gap = (next_session["startTime"] - et_dt).total_seconds() / 60
        if gap < turn_min:
            return False
        if gap > turn_max and not gap_intersects_blockout(hall_key, et_dt, next_session["startTime"], ctx.blockouts_by_hall):
            return False

    if ctx.params["rule1_enabled"]:
        identity = movie_identity_key(session.get("movieNum"), session.get("movieName"))
        for s in all_sessions:
            if movie_identity_key(s.get("movieNum"), s.get("movieName")) != identity:
                continue
            gap = abs((s["startTime"] - st_dt).total_seconds()) / 60
            if gap < int(ctx.params["rule1_gap"]):
                return False

    # 点映时段限制（previewShowTime）
    identity = movie_policy_key(session.get("movieName", ""), session.get("movieMediaType", ""))
    if identity in ctx.preview_windows_by_identity:
        allowed_windows = ctx.preview_windows_by_identity.get(identity, [])
        if not allowed_windows:
            return False
        if not any(w_st <= st_dt <= w_et for w_st, w_et in allowed_windows):
            return False

    return True


def construct_weight(
    movie: Dict[str, Any],
    start_dt: datetime,
    in_tms: bool,
    total_counter: Counter,
    golden_counter: Counter,
    ctx: RuleContext,
) -> float:
    mv = movie_policy_key(movie.get("movieName", ""), movie.get("movieMediaType", ""))
    target = ctx.movie_targets.get(mv, {"min_total": 1, "max_total": 6, "min_golden": 0})
    mc = ctx.manual_constraints.get(mv, {})

    cur_total = int(total_counter.get(mv, 0))
    cur_golden = int(golden_counter.get(mv, 0))

    deficit_total = max(0, int(target.get("min_total", 0)) - cur_total)
    deficit_golden = max(0, int(target.get("min_golden", 0)) - cur_golden)
    over_total = max(0, cur_total - int(target.get("max_total", cur_total + 10)))

    is_golden = ctx.golden_start_dt <= start_dt <= ctx.golden_end_dt

    w = float(ctx.movie_weights.get(mv, target.get("base_weight", 1.0)))
    w *= 1.0 + deficit_total * 0.7

    fixed_sessions = mc.get("fixed_sessions")
    min_sessions = mc.get("min_sessions")
    max_sessions = mc.get("max_sessions")
    min_golden_sessions = mc.get("min_golden_sessions")
    max_golden_sessions = mc.get("max_golden_sessions")

    if fixed_sessions is not None:
        if cur_total < int(fixed_sessions):
            w *= 1.6
        else:
            w *= 0.12
    else:
        if min_sessions is not None and cur_total < int(min_sessions):
            w *= 1.3 + max(0, int(min_sessions) - cur_total) * 0.2
        if max_sessions is not None and cur_total >= int(max_sessions):
            w *= 0.1

    if is_golden:
        w *= 1.05 + deficit_golden * 0.65
        if min_golden_sessions is not None and cur_golden < int(min_golden_sessions):
            w *= 1.25
        if max_golden_sessions is not None and cur_golden >= int(max_golden_sessions):
            w *= 0.2
    elif deficit_golden > 0:
        w *= 0.85

    if over_total > 0:
        w *= max(0.2, 0.8 - over_total * 0.15)

    if in_tms:
        w *= 1.06
    else:
        w *= 0.75

    w *= random.uniform(0.90, 1.15)
    return max(0.01, w)


def simulate_one_candidate(
    movies: List[Dict[str, Any]],
    hall_name_map: Dict[Any, str],
    locked_sessions: List[Dict[str, Any]],
    ctx: RuleContext,
    fail_reason_out: Optional[List[str]] = None,
) -> Optional[List[Dict[str, Any]]]:
    turn_base = int(ctx.params["turnaround_base"])
    turn_min = max(1, turn_base - 3)
    turn_max = max(turn_min, turn_base + 5)

    schedule = [dict(s) for s in locked_sessions]
    by_hall: Dict[str, List[Dict[str, Any]]] = defaultdict(list)
    for s in schedule:
        by_hall[str(s["hallId"])].append(s)

    total_counter = Counter(movie_policy_key(s.get("movieName", ""), s.get("movieMediaType", "")) for s in schedule)
    golden_counter = Counter(
        movie_policy_key(s.get("movieName", ""), s.get("movieMediaType", ""))
        for s in schedule
        if ctx.golden_start_dt <= s["startTime"] <= ctx.golden_end_dt
    )

    forbidden_set = {extract_hall_no(h) for h in ctx.params["rule13_forbidden_halls"]}
    missing_tms_pairs: Set[Tuple[str, str, str]] = set()

    for s in schedule:
        hall_key = extract_hall_no(s.get("hallId") or s.get("hallName"))
        if not session_in_tms(s, hall_key, ctx.tms_by_hall):
            missing_tms_pairs.add(tms_missing_pair_key(s))

    if len(missing_tms_pairs) > int(ctx.params["tms_allowance"]):
        if fail_reason_out is not None:
            fail_reason_out.append(
                f"构造前失败：已售锁定场次导致TMS缺片去重 {len(missing_tms_pairs)} 超过允许值 {int(ctx.params['tms_allowance'])}"
            )
        return None

    min_duration = min(
        [int(m.get("movieDuration") or 9999) for m in movies if int(m.get("movieDuration") or 0) > 0] or [90]
    )

    hall_items = list(hall_name_map.items())
    random.shuffle(hall_items)

    density_window = int(ctx.params.get("rule2_window_minutes", 30))
    density_threshold = int(ctx.params.get("rule2_threshold", 4))
    spread_step = max(5, min(20, int(density_window / max(2, density_threshold + 1))))

    for hall_idx, (hall_id, hall_name) in enumerate(hall_items):
        hall_key = str(hall_id)
        hall_no = extract_hall_no(hall_name or hall_id)
        hall_sessions = by_hall.get(hall_key, [])
        blockouts = ctx.blockouts_by_hall.get(hall_key, [])

        # 各厅首场按步长错峰启动，避免 10:00~10:20 集中扎堆
        base_offset = hall_idx * spread_step
        jitter = random.choice([0, 5, 10])
        cursor = ceil_datetime_to_step(ctx.business_start_dt + timedelta(minutes=base_offset + jitter), 5)
        attempts = 0

        while cursor < ctx.business_end_dt and attempts < 1000:
            attempts += 1
            cursor = ceil_datetime_to_step(cursor, 5)

            occupied = sorted(
                hall_sessions + [{"startTime": b[0], "endTime": b[1], "is_block": True} for b in blockouts],
                key=lambda x: x["startTime"],
            )

            next_anchor = None
            moved = False
            for item in occupied:
                if item["endTime"] <= cursor:
                    continue
                if item["startTime"] <= cursor < item["endTime"]:
                    cursor = item["endTime"]
                    moved = True
                    break
                if item["startTime"] > cursor:
                    next_anchor = item
                    break
            if moved:
                continue

            if cursor >= ctx.business_end_dt:
                break

            gap_end = next_anchor["startTime"] if next_anchor else ctx.business_end_dt
            if (gap_end - cursor).total_seconds() / 60 < min_duration:
                cursor += timedelta(minutes=5)
                continue

            candidates: List[Tuple[Dict[str, Any], float, bool]] = []
            offsets = [0, 5, 10, 15, 20, 25, 30]
            random.shuffle(offsets)

            # 所有算法生成场次的开场时间统一按 5 分钟粒度对齐

            for movie in movies:
                mv_policy = movie_policy_key(movie.get("movieName", ""), movie.get("movieMediaType", ""))
                if ctx.allowed_movies and mv_policy not in ctx.allowed_movies:
                    continue

                dur = int(movie.get("movieDuration") or 0)
                if dur <= 0:
                    continue

                media = movie.get("movieMediaType", "")
                if ctx.params["rule13_enabled"] and hall_no in forbidden_set and is_3d_by_movie_num_or_media(movie.get("movieNum"), media):
                    continue

                for off in offsets:
                    st_dt = cursor + timedelta(minutes=off)
                    et_dt = st_dt + timedelta(minutes=dur)
                    if et_dt > gap_end or et_dt > ctx.business_end_dt:
                        continue

                    cand = {
                        "hallId": hall_id,
                        "hallName": hall_name,
                        "movieId": movie.get("movieId"),
                        "movieNum": movie.get("movieNum"),
                        "movieName": movie.get("movieName", "未知影片"),
                        "movieDuration": dur,
                        "movieMediaType": movie.get("movieMediaType", ""),
                        "startTime": st_dt,
                        "endTime": et_dt,
                        "is_presold": False,
                        "sold": 0,
                    }

                    if not can_place(
                        session=cand,
                        hall_sessions=hall_sessions,
                        all_sessions=schedule,
                        turn_min=turn_min,
                        turn_max=turn_max,
                        hall_key=hall_key,
                        ctx=ctx,
                    ):
                        continue

                    in_tms = session_in_tms(cand, hall_no or hall_key, ctx.tms_by_hall)
                    if not in_tms:
                        cand_key = tms_missing_pair_key(cand)
                        if cand_key not in missing_tms_pairs and len(missing_tms_pairs) >= int(ctx.params["tms_allowance"]):
                            continue

                    w = construct_weight(cand, st_dt, in_tms, total_counter, golden_counter, ctx)

                    # 全局开场密度抑制：优先抑制“前30分钟内已过密”的候选
                    existing_in_window = int(
                        sum(
                            1
                            for s in schedule
                            if 0 <= (st_dt - s["startTime"]).total_seconds() / 60 < density_window
                        )
                    )
                    if existing_in_window >= density_threshold and ctx.params.get("rule2_enabled", True):
                        continue
                    if existing_in_window > 0:
                        w *= max(0.30, 1.0 - 0.10 * existing_in_window)
                    if existing_in_window >= max(0, density_threshold - 1):
                        overflow = existing_in_window - density_threshold + 1
                        w *= max(0.05, 1.0 - 0.22 * overflow)

                    candidates.append((cand, w, in_tms))

            if not candidates:
                cursor += timedelta(minutes=5)
                continue

            chosen, _, in_tms = random.choices(
                population=[c[0] for c in candidates],
                weights=[c[1] for c in candidates],
                k=1,
            )[0], None, None

            for c in candidates:
                if c[0] is chosen:
                    in_tms = c[2]
                    break

            schedule.append(chosen)
            hall_sessions.append(chosen)
            by_hall[hall_key] = hall_sessions

            mv_clean = movie_policy_key(chosen.get("movieName", ""), chosen.get("movieMediaType", ""))
            total_counter[mv_clean] += 1
            if ctx.golden_start_dt <= chosen["startTime"] <= ctx.golden_end_dt:
                golden_counter[mv_clean] += 1

            if in_tms is False:
                missing_tms_pairs.add(tms_missing_pair_key(chosen))

            cursor = ceil_datetime_to_step(chosen["endTime"] + timedelta(minutes=turn_min), 5)

    return schedule


def validate_manual_movie_constraints(
    schedule: List[Dict[str, Any]],
    constraints: Dict[str, Dict[str, Optional[float]]],
    ctx: RuleContext,
    locked_sessions: Optional[List[Dict[str, Any]]] = None,
) -> List[str]:
    if not constraints:
        return []

    df = pd.DataFrame(schedule).copy()
    if df.empty:
        return []
    df["movieClean"] = df.apply(lambda r: movie_policy_key(r.get("movieName", ""), r.get("movieMediaType", "")), axis=1)

    total_sessions = len(df)
    violations: List[str] = []
    locked_total: Counter = Counter()
    locked_golden: Counter = Counter()
    if locked_sessions:
        locked_df = pd.DataFrame(locked_sessions).copy()
        if not locked_df.empty:
            locked_df["movieClean"] = locked_df.apply(
                lambda r: movie_policy_key(r.get("movieName", ""), r.get("movieMediaType", "")), axis=1
            )
            locked_total = Counter(locked_df["movieClean"].tolist())
            locked_golden = Counter(
                locked_df[
                    (locked_df["startTime"] >= ctx.golden_start_dt) & (locked_df["startTime"] <= ctx.golden_end_dt)
                ]["movieClean"].tolist()
            )

    for mv, c in constraints.items():
        sub = df[df["movieClean"] == mv]
        total = int(len(sub))
        golden = int(
            ((sub["startTime"] >= ctx.golden_start_dt) & (sub["startTime"] <= ctx.golden_end_dt)).sum()
        )
        share_pct = (total / total_sessions * 100.0) if total_sessions > 0 else 0.0
        golden_ratio_pct = (golden / total * 100.0) if total > 0 else 0.0

        fixed_sessions = c.get("fixed_sessions")
        min_sessions = c.get("min_sessions")
        max_sessions = c.get("max_sessions")
        min_share_pct = c.get("min_share_pct")
        max_share_pct = c.get("max_share_pct")
        min_golden_sessions = c.get("min_golden_sessions")
        max_golden_sessions = c.get("max_golden_sessions")
        min_golden_ratio_pct = c.get("min_golden_ratio_pct")
        max_golden_ratio_pct = c.get("max_golden_ratio_pct")
        locked_total_mv = int(locked_total.get(mv, 0))
        locked_golden_mv = int(locked_golden.get(mv, 0))

        if max_sessions is not None:
            max_sessions = max(float(max_sessions), float(locked_total_mv))
        if max_golden_sessions is not None:
            max_golden_sessions = max(float(max_golden_sessions), float(locked_golden_mv))
        if fixed_sessions is not None and locked_total_mv > int(fixed_sessions):
            # 预售锁定优先：固定场次不可低于已售锁定
            fixed_sessions = float(locked_total_mv)

        if fixed_sessions is not None and total != int(fixed_sessions):
            violations.append(f"《{mv}》固定场次要求 {int(fixed_sessions)}，当前 {total}")
            continue

        if min_sessions is not None and total < int(min_sessions):
            violations.append(f"《{mv}》次日场次 {total} 低于最少场次 {int(min_sessions)}")
        if min_share_pct is not None and share_pct < float(min_share_pct):
            violations.append(f"《{mv}》排片占比 {share_pct:.1f}% 低于 {float(min_share_pct):.1f}%")
        if max_share_pct is not None and share_pct > float(max_share_pct):
            violations.append(f"《{mv}》排片占比 {share_pct:.1f}% 高于 {float(max_share_pct):.1f}%")
        if min_golden_sessions is not None and golden < int(min_golden_sessions):
            violations.append(f"《{mv}》次日黄金场次 {golden} 低于 {int(min_golden_sessions)}")
        if min_golden_ratio_pct is not None and total > 0 and golden_ratio_pct < float(min_golden_ratio_pct):
            violations.append(f"《{mv}》黄金占比 {golden_ratio_pct:.1f}% 低于 {float(min_golden_ratio_pct):.1f}%")
        if max_golden_ratio_pct is not None and total > 0 and golden_ratio_pct > float(max_golden_ratio_pct):
            violations.append(f"《{mv}》黄金占比 {golden_ratio_pct:.1f}% 高于 {float(max_golden_ratio_pct):.1f}%")

    return violations


def validate_hard_rules(
    schedule: List[Dict[str, Any]],
    locked_sessions: List[Dict[str, Any]],
    ctx: RuleContext,
) -> List[str]:
    if not schedule:
        return ["方案为空"]

    p = ctx.params
    turn_base = int(p["turnaround_base"])
    turn_min = max(1, turn_base - 3)
    turn_max = max(turn_min, turn_base + 5)

    violations: List[str] = []

    for s in schedule:
        st_dt = s["startTime"]
        et_dt = s["endTime"]
        if et_dt <= st_dt:
            violations.append("存在结束时间早于开始时间的场次")
            break
        if st_dt < ctx.business_start_dt or et_dt > ctx.business_end_dt:
            violations.append("存在场次超出营业时间")
            break

    by_hall: Dict[str, List[Dict[str, Any]]] = defaultdict(list)
    for s in schedule:
        by_hall[str(s.get("hallId"))].append(s)

    for hall_key, sessions in by_hall.items():
        sessions = sorted(sessions, key=lambda x: x["startTime"])

        for i in range(1, len(sessions)):
            a = sessions[i - 1]
            b = sessions[i]
            if interval_overlaps(a["startTime"], a["endTime"], b["startTime"], b["endTime"]):
                violations.append(f"影厅{hall_key}存在场次重叠")
                break

            gap = (b["startTime"] - a["endTime"]).total_seconds() / 60
            if gap < turn_min:
                violations.append(f"影厅{hall_key}存在小于{turn_min}分钟的转换间隔")
                break
            if gap > turn_max and not gap_intersects_blockout(hall_key, a["endTime"], b["startTime"], ctx.blockouts_by_hall):
                violations.append(f"影厅{hall_key}存在大于{turn_max}分钟的转换间隔")
                break

    if p["rule1_enabled"]:
        movie_slots: Dict[str, List[datetime]] = defaultdict(list)
        for s in schedule:
            identity = movie_identity_key(s.get("movieNum"), s.get("movieName"))
            movie_slots[identity].append(s["startTime"])
        for identity, starts in movie_slots.items():
            starts = sorted(starts)
            for i in range(1, len(starts)):
                gap = (starts[i] - starts[i - 1]).total_seconds() / 60
                if gap < int(p["rule1_gap"]):
                    violations.append(f"同影片开场间隔小于{int(p['rule1_gap'])}分钟（{identity}）")
                    break

    if p["rule4_enabled"]:
        earliest = min(s["startTime"] for s in schedule).time()
        latest = max(s["startTime"] for s in schedule).time()
        if earliest > parse_hm(p["rule4_earliest"], "10:00"):
            violations.append("最早一场晚于规则四阈值")
        if latest < parse_hm(p["rule4_latest"], "22:30"):
            violations.append("最晚一场早于规则四阈值")

    if p["rule13_enabled"]:
        forbidden_set = {extract_hall_no(h) for h in p["rule13_forbidden_halls"]}
        for s in schedule:
            hall_no = extract_hall_no(s.get("hallName") or s.get("hallId"))
            if hall_no in forbidden_set and is_3d_by_movie_num_or_media(s.get("movieNum"), s.get("movieMediaType", "")):
                violations.append(f"规则十三违规：{hall_no}号厅出现3D")
                break

    if ctx.tms_by_hall:
        missing_pairs: Set[Tuple[str, str, str]] = set()
        for s in schedule:
            hall_no = extract_hall_no(s.get("hallName") or s.get("hallId"))
            if not session_in_tms(s, hall_no, ctx.tms_by_hall):
                missing_pairs.add(tms_missing_pair_key(s))
        if len(missing_pairs) > int(p["tms_allowance"]):
            violations.append(f"TMS 缺片场次(同片同厅去重) {len(missing_pairs)}，超过允许值 {int(p['tms_allowance'])}")

    locked_keys = {
        (
            str(s.get("hallId")),
            movie_policy_key(s.get("movieName", ""), s.get("movieMediaType", "")),
            s.get("startTime"),
            s.get("endTime"),
        )
        for s in locked_sessions
    }
    cand_keys = {
        (
            str(s.get("hallId")),
            movie_policy_key(s.get("movieName", ""), s.get("movieMediaType", "")),
            s.get("startTime"),
            s.get("endTime"),
        )
        for s in schedule
        if s.get("is_presold")
    }
    if not locked_keys.issubset(cand_keys):
        violations.append("存在已售锁定场次被改动")

    manual_violations = validate_manual_movie_constraints(schedule, ctx.manual_constraints, ctx, locked_sessions)
    if manual_violations:
        violations.extend(manual_violations[:20])

    return violations


def normalize_reject_reason(msg: str) -> str:
    text = str(msg or "")
    if not text:
        return "其他淘汰原因"
    if "构造失败" in text:
        return "构造阶段失败"
    if "存在场次重叠" in text:
        return "硬规则：影厅场次重叠"
    if "转换间隔" in text:
        return "硬规则：影厅场次转换间隔不符"
    if "同影片开场间隔" in text:
        return "硬规则：规则一同影片间隔不足"
    if "最早一场晚于" in text:
        return "硬规则：规则四最早场过晚"
    if "最晚一场早于" in text:
        return "硬规则：规则四最晚场过早"
    if "规则十三违规" in text:
        return "硬规则：规则十三禁3D违规"
    if "TMS 缺片场次" in text:
        return "硬规则：TMS缺片超限"
    if "已售锁定场次被改动" in text:
        return "硬规则：预售锁定场次被改动"
    if "固定场次要求" in text:
        return "微调约束：固定场次不满足"
    if "低于最少场次" in text:
        return "微调约束：低于最少场次"
    if "高于最多场次" in text:
        return "微调约束：高于最多场次"
    if "排片占比" in text and "低于" in text:
        return "微调约束：低于最低场次占比"
    if "排片占比" in text and "高于" in text:
        return "微调约束：高于最高场次占比"
    if "黄金场次" in text and "低于" in text:
        return "微调约束：低于最少黄金场次"
    if "黄金场次" in text and "高于" in text:
        return "微调约束：高于最多黄金场次"
    if "黄金占比" in text and "低于" in text:
        return "微调约束：低于最低黄金占比"
    if "黄金占比" in text and "高于" in text:
        return "微调约束：高于最高黄金占比"
    if "超出营业时间" in text:
        return "硬规则：场次超出营业时间"
    if "结束时间早于开始时间" in text:
        return "硬规则：结束时间早于开始时间"
    if "方案为空" in text:
        return "硬规则：空方案"
    return "其他淘汰原因"


def score_efficiency_rules(
    sched_df: pd.DataFrame,
    today_eff: pd.DataFrame,
    locked_sessions: List[Dict[str, Any]],
    ctx: RuleContext,
) -> Tuple[float, str]:
    if today_eff.empty:
        return 0.0, "无今日效率数据"

    bonus = 0.0
    reason_parts: List[str] = []

    golden_mask = (sched_df["startTime"] >= ctx.golden_start_dt) & (sched_df["startTime"] <= ctx.golden_end_dt)
    sim_total = sched_df.groupby("movieClean").size().to_dict()
    sim_golden = sched_df[golden_mask].groupby("movieClean").size().to_dict()

    if locked_sessions:
        locked_df = pd.DataFrame(locked_sessions)
        locked_df["movieClean"] = locked_df.apply(lambda r: movie_policy_key(r.get("movieName", ""), r.get("movieMediaType", "")), axis=1)
        locked_total = locked_df.groupby("movieClean").size().to_dict()
    else:
        locked_total = {}

    for _, row in today_eff.iterrows():
        mv = movie_policy_key(row["影片"])
        today_total = int(row.get("场次", 0))
        today_golden = int(row.get("黄金场次", 0) or 0)
        fe = float(row.get("场次效率", 0) or 0)
        ge = float(row.get("黄金效率", 0) or 0)

        t_total = int(sim_total.get(mv, 0))
        t_golden = int(sim_golden.get(mv, 0))
        locked_cnt = int(locked_total.get(mv, 0))

        if t_total < locked_cnt:
            t_total = locked_cnt

        if today_total == 1:
            if today_golden == 0:
                if fe > 1.5:
                    bonus += 30 if (t_total >= 2 and t_golden >= 1) else -35
                elif fe < 0.5:
                    bonus += 8 if t_total <= 1 else -8
                else:
                    bonus += 4
            else:
                if ge > 1.5:
                    bonus += 30 if (t_total >= 2 and t_golden >= 2) else -35
                elif ge < 0.5:
                    if locked_cnt >= today_total:
                        reason_parts.append(f"{mv}: 锁定场次不可减黄金场，跳过扣分")
                    else:
                        bonus += 12 if t_golden <= 0 else -16
                else:
                    bonus += 5
        else:
            if today_golden == 0:
                if fe > 1.5:
                    bonus += 22 if (t_total >= today_total + 1 and t_golden >= 1) else -24
                elif fe < 0.5:
                    if locked_cnt >= today_total:
                        reason_parts.append(f"{mv}: 锁定场次不可降总量，跳过扣分")
                    else:
                        bonus += 16 if t_total <= max(0, today_total - 1) else -18
                else:
                    bonus += 4
            else:
                if fe > 1.5 and ge > 1.5:
                    bonus += 24 if (t_total >= today_total + 1 and t_golden >= today_golden + 1) else -25
                elif fe > 1.5 and 0.5 <= ge <= 1.5:
                    bonus += 18 if t_total >= today_total + 1 else -16
                elif fe > 1.5 and ge < 0.5:
                    if locked_cnt >= today_golden:
                        reason_parts.append(f"{mv}: 黄金低效但锁定场次不可减，跳过扣分")
                    else:
                        bonus += 12 if (t_total >= today_total + 1 and t_golden <= max(0, today_golden - 1)) else -20
                elif 0.5 <= fe <= 1.5 and ge > 1.5:
                    bonus += 14 if t_golden >= today_golden + 1 else -12
                elif 0.5 <= fe <= 1.5 and ge < 0.5:
                    if locked_cnt >= today_total:
                        reason_parts.append(f"{mv}: 锁定场次不可减，跳过扣分")
                    else:
                        bonus += 10 if (t_total <= today_total - 1 and t_golden <= max(0, today_golden - 1)) else -14
                elif fe < 0.5 and ge > 1.5:
                    if locked_cnt >= today_total:
                        reason_parts.append(f"{mv}: 锁定场次不可降总场，跳过扣分")
                    else:
                        bonus += 9 if (t_total <= max(1, today_total - 1) and t_golden >= today_golden + 1) else -12
                elif fe < 0.5 and 0.5 <= ge <= 1.5:
                    if locked_cnt >= today_total:
                        reason_parts.append(f"{mv}: 锁定场次不可降总场，跳过扣分")
                    else:
                        bonus += 8 if t_total <= max(1, today_total - 1) else -10
                elif fe < 0.5 and ge < 0.5:
                    if locked_cnt >= today_total:
                        reason_parts.append(f"{mv}: 锁定场次不可降总场，跳过扣分")
                    else:
                        bonus += 12 if (t_total <= max(1, today_total - 1) and t_golden <= max(0, today_golden - 1)) else -15

    return bonus, "；".join(reason_parts[:8])


def score_rule2_density(df: pd.DataFrame, ctx: RuleContext) -> Tuple[float, str]:
    p = ctx.params
    if not p["rule2_enabled"]:
        return 0.0, "未启用"

    deduct = 0.0
    starts = sorted(df["startTime"].tolist())
    exempt_ranges = parse_exempt_ranges(p["rule2_exempt_ranges"])

    for st_dt in starts:
        we = st_dt + timedelta(minutes=int(p["rule2_window_minutes"]))
        cnt = int(((df["startTime"] >= st_dt) & (df["startTime"] < we)).sum())
        overflow = cnt - int(p["rule2_threshold"])
        if overflow > 0 and not in_any_exempt(st_dt, exempt_ranges):
            deduct += overflow * float(p["rule2_penalty"])

    return -deduct, f"过密窗口扣分 {deduct:.1f}"


def score_rule3_gap(df: pd.DataFrame, ctx: RuleContext) -> Tuple[float, str]:
    p = ctx.params
    if not p["rule3_enabled"]:
        return 0.0, "未启用"

    deduct = 0.0
    starts = sorted(df["startTime"].tolist())
    if len(starts) <= 1:
        return 0.0, "场次不足"

    for i in range(len(starts) - 1):
        gap = (starts[i + 1] - starts[i]).total_seconds() / 60
        if gap > int(p["rule3_gap_minutes"]):
            if gap_intersects_any_blockout(starts[i], starts[i + 1], ctx.blockouts_by_hall):
                continue
            overflow = max(1.0, gap - int(p["rule3_gap_minutes"]))
            deduct += (overflow / 10.0) * float(p["rule3_penalty"])

    return -deduct, f"全局开场断档扣分 {deduct:.1f}"


def score_rule9_hot_density(df: pd.DataFrame, ctx: RuleContext, box_office_data: List[Dict[str, Any]]) -> Tuple[float, str]:
    p = ctx.params
    if not p["rule9_enabled"]:
        return 0.0, "未启用"

    windows = rule9_core_windows(ctx.target_date)
    golden_df = df[df["startTime"].dt.time.apply(lambda t: time_in_ranges(t, windows))]
    if golden_df.empty:
        return -float(p["rule9_penalty"]), "核心黄金窗口无场次"

    hot_movies, source, _ = resolve_hot_movies(df, box_office_data, int(p["rule9_hot_top_n"]))
    if not hot_movies:
        return -float(p["rule9_penalty"]), "无热门片可评估"

    total = len(golden_df)
    miss = 0
    for mv in hot_movies:
        ratio = float((golden_df["movieClean"] == mv).sum()) / total
        if ratio < float(p["rule9_min_ratio"]):
            miss += 1

    deduct = miss * float(p["rule9_penalty"])
    return -deduct, f"热门片来源:{source}，密度不足 {miss} 部"


def score_rule11_late_hot(df: pd.DataFrame, ctx: RuleContext, box_office_data: List[Dict[str, Any]]) -> Tuple[float, str]:
    p = ctx.params
    if not p["rule11_enabled"]:
        return 0.0, "未启用"

    hot_movies, source, bo_ranked = resolve_hot_movies(df, box_office_data, int(p["rule9_hot_top_n"]))
    top_movies = hot_movies[:3] if hot_movies else []
    if bo_ranked and source == "全国大盘票房":
        top_movies = [m for m, _ in bo_ranked[:3]]

    hot_movies = top_movies
    if not hot_movies:
        return 0.0, "无热门片"

    after_t = parse_hm(p["rule11_after_time"], "22:00")
    late_df = df[df["startTime"].dt.time.apply(lambda t: t >= after_t or t < dt_time(6, 0))]
    if late_df.empty:
        return -float(p["rule11_penalty"]), "22:00后无场次"

    late_movies = set(late_df["movieClean"])
    if any(m in late_movies for m in hot_movies):
        return 0.0, f"热门片来源:{source}，符合"
    return -float(p["rule11_penalty"]), f"热门片来源:{source}，22:00后无热门片"


def score_rule12_top5_golden(df: pd.DataFrame, ctx: RuleContext, box_office_data: List[Dict[str, Any]]) -> Tuple[float, str]:
    p = ctx.params
    if not p["rule12_enabled"]:
        return 0.0, "未启用"

    bo_ranked = sort_movies_by_box_office(box_office_data)
    if not bo_ranked:
        return 0.0, "未获取到次日票房数据"

    top5 = [m for m, _ in bo_ranked[:5]]
    golden_movies = set(df[df["startTime"].dt.time.apply(lambda t: dt_time(14, 0) <= t <= dt_time(21, 0))]["movieClean"])
    miss = [m for m in top5 if m and m not in golden_movies]
    deduct = len(miss) * float(p["rule12_penalty_each"])
    return -deduct, f"缺黄金场影片 {len(miss)}"


def score_manual_upper_constraints(
    schedule: List[Dict[str, Any]],
    constraints: Dict[str, Dict[str, Optional[float]]],
    locked_sessions: List[Dict[str, Any]],
) -> Tuple[float, str]:
    """
    将“最多场次 / 最多黄金场次”作为软扣分项，不再作为硬淘汰项。
    预售锁定优先：若已售锁定本身超过上限，则不对该部分扣分。
    """
    if not constraints or not schedule:
        return 0.0, "无"

    df = pd.DataFrame(schedule).copy()
    if df.empty:
        return 0.0, "无"
    df["movieClean"] = df.apply(lambda r: movie_policy_key(r.get("movieName", ""), r.get("movieMediaType", "")), axis=1)
    totals = Counter(df["movieClean"].tolist())
    golden = Counter(
        df[df["startTime"].dt.time.apply(lambda t: dt_time(14, 0) <= t <= dt_time(21, 0))]["movieClean"].tolist()
    )

    locked_total: Counter = Counter()
    locked_golden: Counter = Counter()
    if locked_sessions:
        ldf = pd.DataFrame(locked_sessions).copy()
        if not ldf.empty:
            ldf["movieClean"] = ldf.apply(lambda r: movie_policy_key(r.get("movieName", ""), r.get("movieMediaType", "")), axis=1)
            locked_total = Counter(ldf["movieClean"].tolist())
            locked_golden = Counter(
                ldf[ldf["startTime"].dt.time.apply(lambda t: dt_time(14, 0) <= t <= dt_time(21, 0))]["movieClean"].tolist()
            )

    # 扣分力度：每超1场分别扣 8 / 10 分
    penalty = 0.0
    lines: List[str] = []
    for mv, c in constraints.items():
        cur_total = int(totals.get(mv, 0))
        cur_golden = int(golden.get(mv, 0))
        max_total = c.get("max_sessions")
        max_golden = c.get("max_golden_sessions")

        if max_total is not None:
            eff_max_total = max(int(max_total), int(locked_total.get(mv, 0)))
            overflow = max(0, cur_total - eff_max_total)
            if overflow > 0:
                d = overflow * 8.0
                penalty += d
                lines.append(f"{mv} 超总场 {overflow} 场(-{d:.0f})")

        if max_golden is not None:
            eff_max_golden = max(int(max_golden), int(locked_golden.get(mv, 0)))
            overflow_g = max(0, cur_golden - eff_max_golden)
            if overflow_g > 0:
                d = overflow_g * 10.0
                penalty += d
                lines.append(f"{mv} 超黄金场 {overflow_g} 场(-{d:.0f})")

    return (-penalty, "；".join(lines[:8]) if lines else "满足上限约束")


def score_candidate(
    schedule: List[Dict[str, Any]],
    ctx: RuleContext,
    today_eff: pd.DataFrame,
    locked_sessions: List[Dict[str, Any]],
    box_office_data: List[Dict[str, Any]],
) -> CandidateResult:
    if not schedule:
        return CandidateResult(schedule=[], score=0.0, score_breakdown=[], hard_violations=["空方案"])

    df = pd.DataFrame(schedule).sort_values(["startTime", "hallId"]).copy()
    df["movieClean"] = df.apply(lambda r: movie_policy_key(r.get("movieName", ""), r.get("movieMediaType", "")), axis=1)

    score = 1000.0
    breakdown: List[Tuple[str, float, str]] = []

    if ctx.params["efficiency_enabled"]:
        delta, msg = score_efficiency_rules(df, today_eff, locked_sessions, ctx)
        penalty_coef = float(ctx.params.get("efficiency_penalty_coef", 1.0) or 1.0)
        if delta < 0:
            delta *= max(0.0, penalty_coef)
        score += delta
        breakdown.append(("效率分析表", delta, msg or "按18种情况评估"))

    d2, m2 = score_rule2_density(df, ctx)
    score += d2
    breakdown.append(("规则二", d2, m2))

    d3, m3 = score_rule3_gap(df, ctx)
    score += d3
    breakdown.append(("规则三", d3, m3))

    d9, m9 = score_rule9_hot_density(df, ctx, box_office_data)
    score += d9
    breakdown.append(("规则九", d9, m9))

    d11, m11 = score_rule11_late_hot(df, ctx, box_office_data)
    score += d11
    breakdown.append(("规则十一", d11, m11))

    d12, m12 = score_rule12_top5_golden(df, ctx, box_office_data)
    score += d12
    breakdown.append(("规则十二", d12, m12))

    d_manual_max, m_manual_max = score_manual_upper_constraints(schedule, ctx.manual_constraints, locked_sessions)
    score += d_manual_max
    breakdown.append(("微调上限扣分", d_manual_max, m_manual_max))

    return CandidateResult(schedule=schedule, score=score, score_breakdown=breakdown, hard_violations=[])


def _append_rule_logs(parts: List[str], title: str, logs: List[str]) -> None:
    parts.append(title)
    if logs:
        for i, log in enumerate(logs, 1):
            parts.append(f"{i}. {log}")
    else:
        parts.append("（无）")


def _hall_display(raw: Any, with_ting: bool = True) -> str:
    hall_no = extract_hall_no(raw)
    if not hall_no:
        return str(raw or "")
    return f"{hall_no}号厅" if with_ting else f"{hall_no}号"


def generate_schedule_check_logs_text(
    schedule: List[Dict[str, Any]],
    target_date: date,
    params: Dict[str, Any],
    today_eff: pd.DataFrame,
    box_office_data: List[Dict[str, Any]],
) -> str:
    if not schedule:
        return "无排片数据，无法进行合理性检查。"

    df = pd.DataFrame(schedule).copy()
    if df.empty:
        return "无排片数据，无法进行合理性检查。"

    df["startTime"] = pd.to_datetime(df["startTime"], errors="coerce")
    df["endTime"] = pd.to_datetime(df["endTime"], errors="coerce")
    df = df.dropna(subset=["startTime", "endTime"]).sort_values("startTime").reset_index(drop=True)
    if df.empty:
        return "无有效排片时间数据，无法进行合理性检查。"

    df["filmName"] = df["movieName"].astype(str)
    df["clean_filmName"] = df["filmName"].apply(clean_movie_title)
    df["simpleHallName"] = df["hallName"].apply(lambda x: _hall_display(x, with_ting=True))

    bo_ranked = sort_movies_by_box_office(box_office_data)
    bo_sorted_movies = [m for m, _ in bo_ranked]
    movie_box_office = {m: float(v) for m, v in bo_ranked}

    final_log_parts: List[str] = []

    # Rule 1
    logs_r1: List[str] = []
    gap_limit = int(params.get("rule1_gap", 30))
    movie_num_series = df["movieNum"] if "movieNum" in df.columns else pd.Series([""] * len(df), index=df.index)
    df["movieSerial_5_8"] = movie_num_series.apply(extract_movie_serial_5_8)
    serial_values = [s for s in df["movieSerial_5_8"].dropna().unique() if str(s).strip()]
    for serial in serial_values:
        film_schedules = df[df["movieSerial_5_8"] == serial].sort_values("startTime").reset_index(drop=True)
        for i in range(len(film_schedules) - 1):
            s1, s2 = film_schedules.iloc[i], film_schedules.iloc[i + 1]
            interval = (s2["startTime"] - s1["startTime"]).total_seconds() / 60
            if interval < gap_limit:
                logs_r1.append(
                    f"《{s1['filmName']}》{s1['simpleHallName']}【{s1['startTime'].strftime('%H:%M')}】和 "
                    f"《{s2['filmName']}》{s2['simpleHallName']}【{s2['startTime'].strftime('%H:%M')}】"
                    f"开场时间距离 {int(interval)} 分钟（年度顺序号:{serial}）"
                )
    _append_rule_logs(
        final_log_parts,
        f"规则一：同影片场次间隔过近（按 movieNum 第5~8位年度顺序号，少于 {gap_limit} 分钟）",
        logs_r1,
    )

    # Rule 2
    logs_r2: List[str] = []
    window_minutes = int(params.get("rule2_window_minutes", 30))
    threshold = int(params.get("rule2_threshold", 4))
    i = 0
    processed_indices_r2 = set()
    while i < len(df):
        if i in processed_indices_r2:
            i += 1
            continue
        window_start_time = df.iloc[i]["startTime"]
        window_end_time = window_start_time + timedelta(minutes=window_minutes)
        window_df = df[(df["startTime"] >= window_start_time) & (df["startTime"] < window_end_time)]
        if len(window_df) > threshold:
            start_t_str = window_df.iloc[0]["startTime"].strftime("%H:%M")
            end_t_str = window_df.iloc[-1]["startTime"].strftime("%H:%M")
            lines = [f"【{start_t_str} - {end_t_str}】开场时间比较集中："]
            for _, row in window_df.iterrows():
                lines.append(f"  {row['simpleHallName']}《{row['filmName']}》> {row['startTime'].strftime('%H:%M')}")
                processed_indices_r2.add(int(row.name))
            logs_r2.append("\n".join(lines))
        i += 1
    _append_rule_logs(final_log_parts, f"\n规则二：{window_minutes} 分钟内影片开场超过 {threshold} 场", logs_r2)

    # Rule 3
    logs_r3: List[str] = []
    gap_minutes = int(params.get("rule3_gap_minutes", 30))
    if len(df) > 1:
        for i in range(len(df) - 1):
            s1_start, s2_start = df.iloc[i]["startTime"], df.iloc[i + 1]["startTime"]
            gap = (s2_start - s1_start).total_seconds() / 60
            if gap > gap_minutes:
                logs_r3.append(f"【{s1_start.strftime('%H:%M')} ~ {s2_start.strftime('%H:%M')}】缺少影片开场，间隔 {int(gap)} 分钟")
    _append_rule_logs(final_log_parts, f"\n规则三：场次开场间隔超过 {gap_minutes} 分钟", logs_r3)

    # Rule 4
    logs_r4: List[str] = []
    if not df.empty:
        first_sched = df.iloc[0]
        last_sched = df.iloc[-1]
        earliest_limit = parse_hm(params.get("rule4_earliest", "10:00"), "10:00")
        latest_limit = parse_hm(params.get("rule4_latest", "22:30"), "22:30")
        if first_sched["startTime"].time() > earliest_limit:
            logs_r4.append(
                f"最早一场 {first_sched['simpleHallName']}《{first_sched['filmName']}》{first_sched['startTime'].strftime('%H:%M')} 晚于 {earliest_limit.strftime('%H:%M')}"
            )
        if last_sched["startTime"].time() < latest_limit:
            logs_r4.append(
                f"最晚一场 {last_sched['simpleHallName']}《{last_sched['filmName']}》{last_sched['startTime'].strftime('%H:%M')} 早于 {latest_limit.strftime('%H:%M')}"
            )
    _append_rule_logs(final_log_parts, "\n规则四：最早一场晚于 10:00，最晚一场早于 22:30", logs_r4)

    # Rule 5
    logs_r5: List[str] = []
    w5_start = datetime.combine(target_date, dt_time(10, 0))
    w5_end = datetime.combine(target_date, dt_time(23, 0))
    for hall_name in df["simpleHallName"].unique():
        hall_df = df[df["simpleHallName"] == hall_name].sort_values("startTime")
        for i in range(len(hall_df) - 1):
            prev_end = hall_df.iloc[i]["endTime"]
            curr_start = hall_df.iloc[i + 1]["startTime"]
            if prev_end < w5_end and curr_start > w5_start:
                idle_mins = (curr_start - prev_end).total_seconds() / 60
                if idle_mins > 60:
                    logs_r5.append(f"{hall_name.replace('厅', '')} 【{prev_end.strftime('%H:%M')} ~ {curr_start.strftime('%H:%M')}】无影片在播，时长 {int(idle_mins)} 分钟")
    _append_rule_logs(final_log_parts, "\n规则五：影厅空闲时间超过 1 小时（10:00-23:00）", logs_r5)

    # Rule 6
    logs_r6: List[str] = []
    convert_limit = int(params.get("turnaround_base", 10))
    for hall_name in df["simpleHallName"].unique():
        hall_df = df[df["simpleHallName"] == hall_name].sort_values("startTime")
        for i in range(len(hall_df) - 1):
            prev_sched = hall_df.iloc[i]
            next_sched = hall_df.iloc[i + 1]
            conversion = (next_sched["startTime"] - prev_sched["endTime"]).total_seconds() / 60
            if conversion < convert_limit:
                logs_r6.append(
                    f"{hall_name.replace('厅', '')} {prev_sched['endTime'].strftime('%H:%M')} 《{prev_sched['filmName']}》结束后影厅空闲时间仅为 {int(conversion)} 分钟"
                )
    _append_rule_logs(final_log_parts, "\n规则六：影厅场次转换时间检查", logs_r6)

    # Rule 7
    logs_r7: List[str] = []
    if not df.empty:
        current_time = df.iloc[0]["startTime"].replace(second=0, microsecond=0)
        end_time = df.iloc[-1]["endTime"]
        reported_windows = set()
        while current_time < end_time:
            window_end = current_time + timedelta(minutes=10)
            starts_in_window = df[(df["startTime"] >= current_time) & (df["startTime"] < window_end)]
            ends_in_window = df[(df["endTime"] > current_time) & (df["endTime"] <= window_end)]
            if len(starts_in_window) + len(ends_in_window) > 5:
                window_tuple = (current_time.strftime("%H:%M"), window_end.strftime("%H:%M"))
                if window_tuple not in reported_windows:
                    exit_halls = "、".join(sorted(set(ends_in_window["simpleHallName"].tolist())))
                    entry_halls = "、".join(sorted(set(starts_in_window["simpleHallName"].tolist())))
                    log_msg = f"【{current_time.strftime('%H:%M')} ~ {window_end.strftime('%H:%M')}】"
                    if exit_halls:
                        log_msg += f"，{exit_halls}集中散场"
                    if entry_halls:
                        log_msg += "，同时" if exit_halls else "，"
                        log_msg += f"{entry_halls}即将入场"
                    log_msg += "，预计人流瞬时压力过大。"
                    logs_r7.append(log_msg)
                    reported_windows.add(window_tuple)
            current_time += timedelta(minutes=5)

        start_groups = df.groupby("startTime").filter(lambda x: len(x) > 3)
        for time_val, group in start_groups.groupby("startTime"):
            halls = "、".join(sorted(set(group["simpleHallName"].tolist())))
            logs_r7.append(f"{time_val.strftime('%H:%M')}，{halls}电影同时开场，注意预计人流瞬时压力过大。")

        end_groups = df.groupby("endTime").filter(lambda x: len(x) > 3)
        for time_val, group in end_groups.groupby("endTime"):
            halls = "、".join(sorted(set(group["simpleHallName"].tolist())))
            logs_r7.append(f"{time_val.strftime('%H:%M')}，{halls}电影同时散场，注意预计人流瞬时压力过大。")
    _append_rule_logs(final_log_parts, "\n规则七：动态散场和入场高峰预警", logs_r7)

    # Rule 8
    logs_r8: List[str] = []
    for hall_name in df["simpleHallName"].unique():
        hall_df = df[df["simpleHallName"] == hall_name]
        last_sched = hall_df.nlargest(1, "endTime").iloc[0]
        if last_sched["endTime"].date() == target_date and last_sched["endTime"].time() < dt_time(22, 30):
            logs_r8.append(f"{hall_name.replace('厅', '')} 最后一场于【{last_sched['endTime'].strftime('%H:%M')}】结束，过早停运。")
    _append_rule_logs(final_log_parts, "\n规则八：影厅结束运营过早预警", logs_r8)

    # Rule 9
    logs_r9: List[str] = []
    windows = rule9_core_windows(target_date)
    period_str = " 和 ".join([f"{s.strftime('%H:%M')}-{e.strftime('%H:%M')}" for s, e in windows])
    golden_df = df[df["startTime"].apply(lambda x: time_in_ranges(x.time(), windows))]
    if not golden_df.empty:
        if bo_sorted_movies:
            max_bo = float(movie_box_office.get(bo_sorted_movies[0], 0))
            if max_bo > 0:
                hot_films = [m for m, v in movie_box_office.items() if v >= max_bo * 0.95]
            else:
                hot_films = bo_sorted_movies[: int(params.get("rule9_hot_top_n", 3))]
        else:
            counts = df["clean_filmName"].value_counts()
            max_count = int(counts.iloc[0]) if not counts.empty else 0
            hot_films = counts[counts >= max_count * 0.95].index.tolist() if max_count > 0 else []

        min_ratio = float(params.get("rule9_min_ratio", 0.3))
        for film in hot_films:
            ratio = float((golden_df["clean_filmName"] == film).sum()) / max(1, len(golden_df))
            if ratio < min_ratio:
                logs_r9.append(f"《{film}》在核心黄金时段 {period_str} 排片占比仅为{ratio:.1%}，低于 {min_ratio:.0%}。")
    _append_rule_logs(final_log_parts, "\n规则九：黄金时段热门影片排片密度检查", logs_r9)

    # Rule 10
    logs_r10: List[str] = []
    if today_eff is not None and not today_eff.empty:
        tomorrow_stats: Dict[str, Dict[str, int]] = {}
        for film in df["clean_filmName"].unique():
            fdf = df[df["clean_filmName"] == film]
            tom_total = len(fdf)
            tom_golden = len(fdf[fdf["startTime"].apply(lambda x: dt_time(14, 0) <= x.time() <= dt_time(21, 0))])
            tomorrow_stats[film] = {"total": int(tom_total), "golden": int(tom_golden)}

        for _, row in today_eff.iterrows():
            film = clean_movie_title(row.get("影片", ""))
            if film not in tomorrow_stats:
                continue

            today_total = int(row.get("场次", 0) or 0)
            today_golden = int(row.get("黄金场次", 0) or 0)
            fe = float(row.get("场次效率", 0) or 0)
            ge = float(row.get("黄金效率", 0) or 0)
            tom_total = int(tomorrow_stats[film]["total"])
            tom_golden = int(tomorrow_stats[film]["golden"])
            is_valid = True

            if today_total == 1:
                if today_golden == 0:
                    if fe > 1.5:
                        is_valid = tom_golden >= 1 and tom_total >= 2
                    elif fe < 0.5:
                        is_valid = tom_total in [0, 1]
                    else:
                        is_valid = tom_total in [0, 1, 2]
                else:
                    if ge > 1.5:
                        is_valid = tom_golden >= 2 and tom_total >= 2
                    elif ge < 0.5:
                        is_valid = tom_golden == 0 and tom_total in [0, 1]
                    else:
                        is_valid = (tom_total, tom_golden) in [(1, 1), (2, 1), (1, 0)]
            else:
                if today_golden == 0:
                    if fe > 1.5:
                        is_valid = tom_total > today_total and tom_golden >= 1
                    elif fe < 0.5:
                        is_valid = tom_total < today_total and tom_golden == 0
                else:
                    if fe > 1.5 and ge > 1.5:
                        is_valid = tom_total > today_total and tom_golden >= today_golden + 1
                    elif fe > 1.5 and 0.5 <= ge <= 1.5:
                        is_valid = tom_total > today_total
                    elif fe > 1.5 and ge < 0.5:
                        is_valid = tom_total > today_total and tom_golden <= max(0, today_golden - 1)
                    elif 0.5 <= fe <= 1.5 and ge > 1.5:
                        is_valid = tom_golden >= today_golden + 1
                    elif 0.5 <= fe <= 1.5 and ge < 0.5:
                        is_valid = tom_total < today_total and tom_golden <= max(0, today_golden - 1)
                    elif fe < 0.5 and ge > 1.5:
                        is_valid = tom_total <= max(1, today_total - 1) and tom_golden >= today_golden
                    elif fe < 0.5 and 0.5 <= ge <= 1.5:
                        is_valid = tom_total <= max(1, today_total - 1)
                    elif fe < 0.5 and ge < 0.5:
                        is_valid = tom_total <= max(1, today_total - 1) and tom_golden <= max(0, today_golden - 1)

            if not is_valid:
                film_rows = df[df["clean_filmName"] == film]
                locked_cnt = int(film_rows["is_presold"].fillna(False).sum()) if ("is_presold" in film_rows.columns) else 0
                # 预售优先：若次日已有预售锁定场次，与效率建议冲突时可忽略
                if locked_cnt > 0:
                    continue
                logs_r10.append(f"《{film}》全天场次效率：{fe:.2f} 黄金时段场次效率：{ge:.2f} 次日的排片不满足要求。")
    _append_rule_logs(final_log_parts, "\n规则十：次日排片效率匹配度检查", logs_r10)

    # Rule 11
    logs_r11: List[str] = []
    if bo_sorted_movies:
        top_movies = bo_sorted_movies[:3]
        top_movies_type = "票房排行前三"
    else:
        top_movies = df["clean_filmName"].value_counts().head(3).index.tolist()
        top_movies_type = "排片量前三"

    if top_movies:
        after_t = parse_hm(params.get("rule11_after_time", "22:00"), "22:00")
        late_sessions = df[df["startTime"].apply(lambda t: t.time() >= after_t or t.time() < dt_time(6, 0))]
        late_movies = set(late_sessions["clean_filmName"].unique()) if not late_sessions.empty else set()
        if not any(m in late_movies for m in top_movies):
            top_movies_str = "、".join([f"《{m}》" for m in top_movies])
            logs_r11.append(f"{top_movies_type}的影片 {top_movies_str} 在 22:00 后均无场次，建议增加热门影片晚场。")
    _append_rule_logs(final_log_parts, "\n规则十一：22:00 后热门影片排片检查", logs_r11)

    # Rule 12
    logs_r12: List[str] = []
    if bo_sorted_movies:
        for movie in bo_sorted_movies[:5]:
            movie_df = df[df["clean_filmName"] == movie]
            if movie_df.empty:
                logs_r12.append(f"《{movie}》为次日票房排行前五的影片，但目前未排片。")
                continue
            golden_sessions = movie_df[movie_df["startTime"].apply(lambda x: dt_time(14, 0) <= x.time() <= dt_time(21, 0))]
            if golden_sessions.empty:
                logs_r12.append(f"《{movie}》为次日票房排行前五的影片，但没有安排黄金场（14:00-21:00）。")
    else:
        logs_r12.append("未获取到次日票房数据，无法检查规则十二。")
    _append_rule_logs(final_log_parts, "\n规则十二：次日票房前五的影片必须有一场黄金场", logs_r12)

    # Rule 13
    logs_r13: List[str] = []
    restricted = {extract_hall_no(x) for x in params.get("rule13_forbidden_halls", ["2", "8", "9"])}
    for _, row in df.iterrows():
        hall_no = extract_hall_no(row.get("hallName"))
        if hall_no in restricted and is_3d_by_movie_num_or_media(row.get("movieNum"), row.get("movieMediaType", "")):
            logs_r13.append(f"{hall_no}号厅《{row.get('filmName', '未知影片')}》疑似3D排片（movieNum第4位为2）")
    _append_rule_logs(final_log_parts, "\n规则十三：2号、8号、9号厅禁止3D排片检查（movieNum第4位为2）", logs_r13)

    return "\n".join(final_log_parts)


def schedule_signature(schedule: List[Dict[str, Any]]) -> str:
    tokens: List[str] = []
    for s in sorted(
        schedule,
        key=lambda x: (
            str(x.get("hallId")),
            x.get("startTime"),
            movie_policy_key(x.get("movieName", ""), x.get("movieMediaType", "")),
        ),
    ):
        tokens.append(
            f"{s.get('hallId')}|{movie_policy_key(s.get('movieName',''), s.get('movieMediaType',''))}|"
            f"{s.get('startTime').strftime('%H:%M')}|{s.get('endTime').strftime('%H:%M')}"
        )
    return "#".join(tokens)


def render_gantt(schedule: List[Dict[str, Any]], date_str: str, tab_key: str) -> None:
    if not schedule:
        st.info("无排片数据")
        return

    df = pd.DataFrame(schedule).copy()
    if df.empty:
        st.info("无排片数据")
        return

    df["startTime"] = pd.to_datetime(df["startTime"], errors="coerce")
    df["endTime"] = pd.to_datetime(df["endTime"], errors="coerce")
    df = df.dropna(subset=["hallName", "movieName", "startTime", "endTime"]).copy()
    if df.empty:
        st.info("无有效排片数据")
        return

    def _hall_sort_key(h: Any) -> Tuple[int, str]:
        nums = re.findall(r"\d+", str(h))
        return (int(nums[0]), str(h)) if nums else (9999, str(h))

    hall_order = sorted(df["hallName"].astype(str).unique().tolist(), key=_hall_sort_key)
    t_min = df["startTime"].min().replace(minute=0, second=0, microsecond=0)
    t_max = (df["endTime"].max() + timedelta(hours=1)).replace(minute=0, second=0, microsecond=0)
    total_minutes = max(60.0, (t_max - t_min).total_seconds() / 60.0)
    total_hours = max(1, int((t_max - t_min).total_seconds() / 3600))

    palette = [
        "#2A9D8F",
        "#E76F51",
        "#264653",
        "#F4A261",
        "#5B8FF9",
        "#6DC8EC",
        "#5D7092",
        "#9270CA",
        "#FF9D4D",
        "#269A99",
    ]
    movies = sorted(df["movieName"].astype(str).unique().tolist())
    color_map = {m: palette[i % len(palette)] for i, m in enumerate(movies)}

    labels: List[str] = []
    for i in range(total_hours + 1):
        labels.append(f'<div class="time-label">{(t_min + timedelta(hours=i)).strftime("%H:%M")}</div>')
    time_labels_html = "".join(labels)

    halls_html = ""
    for hall in hall_order:
        row_df = df[df["hallName"].astype(str) == hall].sort_values("startTime")
        blocks = ""
        for _, r in row_df.iterrows():
            start = r["startTime"]
            end = r["endTime"]
            left = ((start - t_min).total_seconds() / 60.0 / total_minutes) * 100.0
            width = ((end - start).total_seconds() / 60.0 / total_minutes) * 100.0
            if left < 0:
                width += left
                left = 0
            width = max(0.4, width)
            if left + width > 100:
                width = max(0.4, 100 - left)
            tooltip = (
                f"{r['movieName']}\\n"
                f"{start.strftime('%H:%M')} - {end.strftime('%H:%M')}\\n"
                f"{int((end - start).total_seconds() / 60)}min"
            )
            blocks += (
                f'<div class="schedule-block" style="left:{left:.3f}%;width:{width:.3f}%;'
                f'background-color:{color_map.get(r["movieName"], "#778899")};" title="{tooltip}">'
                f'<div class="block-film-name">{r["movieName"]}</div>'
                f'<div class="block-time-duration">{start.strftime("%H:%M")}-{end.strftime("%H:%M")}</div>'
                "</div>"
            )
        halls_html += (
            f'<div class="hall-row"><div class="hall-name-cell">{hall}</div>'
            f'<div class="timeline-cell">{blocks}</div></div>'
        )

    dt_obj = datetime.strptime(date_str, "%Y-%m-%d")
    weekdays = ["星期一", "星期二", "星期三", "星期四", "星期五", "星期六", "星期日"]
    date_display = f"{dt_obj.strftime('%Y.%m.%d')} {weekdays[dt_obj.weekday()]} • 显示场次：{len(df)}"
    half_hour_grid_size = 100 / max(1, total_hours * 2)

    html = f"""
    <style>
      .scroll-wrapper {{width:100%;overflow-x:auto;border:1px solid #dee2e6;border-radius:8px;margin-bottom:1rem;}}
      .schedule-container {{font-family:-apple-system,BlinkMacSystemFont,"Segoe UI",sans-serif;background:#fff;min-width:1500px;}}
      .header-bar {{display:flex;align-items:center;padding:10px 16px;background:#f8f9fa;border-bottom:1px solid #dee2e6;font-size:15px;font-weight:600;}}
      .chart-grid {{display:grid;grid-template-columns:140px 1fr;}}
      .top-left-corner {{grid-column:1;grid-row:1;border-bottom:1px solid #dee2e6;border-right:1px solid #dee2e6;background:#f8f9fa;}}
      .time-axis-wrapper {{grid-column:2;display:flex;background:#f8f9fa;border-bottom:1px solid #dee2e6;}}
      .time-label {{flex:1;text-align:center;padding:8px 0;font-size:12px;color:#555;border-left:1px solid #d3d3d3;}}
      .hall-row {{display:contents;}}
      .hall-name-cell {{grid-column:1;padding:12px 8px;font-size:13px;font-weight:500;border-right:1px solid #dee2e6;border-top:1px solid #dee2e6;background:#fdfdfd;text-align:center;display:flex;align-items:center;justify-content:center;line-height:1.2;}}
      .timeline-cell {{grid-column:2;position:relative;border-top:1px solid #dee2e6;background-image:linear-gradient(to right,#f0f0f0 1px,transparent 1px),linear-gradient(to right,#d3d3d3 1px,transparent 1px);background-size:{half_hour_grid_size}% 100%, {100 / max(1, total_hours)}% 100%;min-height:64px;}}
      .schedule-block {{position:absolute;top:4px;bottom:4px;border-radius:5px;padding:6px 10px;color:#fff;overflow:hidden;display:flex;flex-direction:column;justify-content:center;align-items:flex-start;box-sizing:border-box;border:1px solid rgba(0,0,0,0.2);box-shadow:0 1px 3px rgba(0,0,0,0.1);cursor:pointer;}}
      .block-film-name,.block-time-duration {{width:100%;white-space:nowrap;overflow:hidden;text-overflow:ellipsis;}}
      .block-film-name {{font-weight:600;font-size:13px;line-height:1.25;}}
      .block-time-duration {{font-size:11px;opacity:0.95;line-height:1.2;}}
    </style>
    <div class="scroll-wrapper">
      <div class="schedule-container">
        <div class="header-bar">{date_display}</div>
        <div class="chart-grid">
          <div class="top-left-corner"></div>
          <div class="time-axis-wrapper">{time_labels_html}</div>
          {halls_html}
        </div>
      </div>
    </div>
    """
    st.markdown(html, unsafe_allow_html=True)


def derive_movies_from_schedule(next_day_schedule: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    pool: Dict[Tuple[Any, str], Dict[str, Any]] = {}
    for s in next_day_schedule:
        movie_id = s.get("movieId")
        movie_name = s.get("movieName")
        if not movie_name:
            continue
        key = (movie_id, clean_movie_title(movie_name))
        if key not in pool:
            pool[key] = {
                "movieId": movie_id,
                "movieNum": s.get("movieNum"),
                "movieName": movie_name,
                "movieDuration": int(s.get("movieLength") or s.get("movieDuration") or 120),
                "movieMediaType": s.get("movieMediaType", ""),
            }
    return list(pool.values())


def df_schedule_for_display(schedule: List[Dict[str, Any]]) -> pd.DataFrame:
    if not schedule:
        return pd.DataFrame()

    df = pd.DataFrame(schedule).sort_values(["hallName", "startTime"])
    df["开始"] = pd.to_datetime(df["startTime"]).dt.strftime("%H:%M")
    df["结束"] = pd.to_datetime(df["endTime"]).dt.strftime("%H:%M")
    out = df[
        ["hallName", "开始", "结束", "movieName", "movieDuration", "movieMediaType", "is_presold", "sold"]
    ].rename(
        columns={
            "hallName": "影厅",
            "movieName": "影片",
            "movieDuration": "片长(分钟)",
            "movieMediaType": "制式",
            "is_presold": "已售锁定",
            "sold": "锁定已售票数",
        }
    )
    return out


def normalize_maintenance_blocks_input(raw_blocks: Any) -> List[Dict[str, Any]]:
    if raw_blocks is None:
        return []

    if isinstance(raw_blocks, pd.DataFrame):
        records = raw_blocks.to_dict("records")
    elif isinstance(raw_blocks, list):
        records = raw_blocks
    elif isinstance(raw_blocks, str):
        try:
            data = json.loads(raw_blocks)
            records = data if isinstance(data, list) else []
        except Exception:
            records = []
    else:
        records = []

    out: List[Dict[str, Any]] = []
    for r in records:
        hall = str((r or {}).get("hall") or (r or {}).get("hallId") or (r or {}).get("hallName") or "").strip()
        st_s = str((r or {}).get("start") or "").strip()
        et_s = str((r or {}).get("end") or "").strip()
        if not hall or not st_s or not et_s:
            continue
        try:
            datetime.strptime(st_s, "%H:%M")
            datetime.strptime(et_s, "%H:%M")
        except Exception:
            continue
        out.append({"hall": hall, "start": st_s, "end": et_s})
    return out


def build_presale_rank_from_schedule(next_day_schedule: List[Dict[str, Any]]) -> List[Tuple[str, float, int]]:
    score_map: Dict[str, float] = defaultdict(float)
    ticket_map: Dict[str, int] = defaultdict(int)
    for s in next_day_schedule or []:
        mv = movie_policy_key(s.get("movieName", ""), s.get("movieMediaType", ""))
        if not mv:
            continue
        sold_tickets = int(s.get("soldTicketNum") or s.get("buyTicketNum") or 0)
        sold_box = float(s.get("soldBoxOffice") or 0.0)
        score_map[mv] += max(0.0, sold_box)
        ticket_map[mv] += max(0, sold_tickets)
    ranked = sorted(score_map.keys(), key=lambda mv: (score_map[mv], ticket_map[mv], mv), reverse=True)
    return [(mv, float(score_map[mv]), int(ticket_map[mv])) for mv in ranked]


def _clamp_int(v: Optional[int], low: int = 0) -> Optional[int]:
    if v is None:
        return None
    return max(low, int(v))


def derive_efficiency_defaults_18_cases(
    *,
    N: int,
    M: int,
    D: float,
    G: float,
    C: int,
) -> Tuple[Optional[int], Optional[int], Optional[int], Optional[int]]:
    """
    根据 18 种策略给出默认范围（总场次最少/最多、黄金场次最少/最多）。
    """
    N = max(0, int(N))
    M = max(0, int(M))
    C = max(0, int(C))
    D = max(0.0, float(D))
    G = max(0.0, float(G))

    theo_total = int(round(N * (D ** 0.5)))
    theo_golden = int(round(M * (G ** 0.5)))

    min_total: Optional[int] = None
    max_total: Optional[int] = None
    min_golden: Optional[int] = None
    max_golden: Optional[int] = None

    if N == 1:
        if M == 0:
            if D >= 1.5:  # case1
                min_total = 2
                max_total = min(1 + C, theo_total + 1)
                min_golden = 1
                max_golden = max_total
            elif D >= 0.5:  # case2
                min_total = 0
                max_total = 2
                min_golden = 0
                max_golden = 1
            else:  # case3
                min_total = 0
                max_total = 1
                min_golden = 0
                max_golden = 0
        else:
            if G >= 1.5:  # case4
                min_total = 2
                max_total = min(1 + C, int(round(1 * (G ** 0.5))) + 1)
                min_golden = 1
                max_golden = max_total
            elif G >= 0.5:  # case5
                min_total = 1
                max_total = 2
                min_golden = 0
                max_golden = 1
            else:  # case6
                min_total = 0
                max_total = 1
                min_golden = 0
                max_golden = 0
    else:
        if M == 0:
            if D >= 1.5:  # case7
                min_total = N + 1
                max_total = max(N + 1, min(N + C, theo_total))
                min_golden = 1
                max_golden = min(max_total, int(round(N * (D ** 0.5) * 0.5)))
            elif D >= 0.5:  # case8
                min_total = max(1, int(round(N * 0.8)))
                max_total = N + 1
                min_golden = 0
                max_golden = 1
            else:  # case9
                min_total = max(0, theo_total)
                max_total = N - 1
                min_golden = 0
                max_golden = 0
        else:
            if D >= 1.5 and G >= 1.5:  # case10
                min_total = N + 1
                max_total = max(N + 1, min(N + C, theo_total))
                min_golden = M + 1
                max_golden = max(M + 1, min(M + C, theo_golden, max_total))
            elif D >= 1.5 and 0.5 <= G < 1.5:  # case11
                min_total = N + 1
                max_total = max(N + 1, min(N + C, theo_total))
                min_golden = M
                max_golden = M + 1
            elif D >= 1.5 and G < 0.5:  # case12
                min_total = N + 1
                max_total = max(N + 1, min(N + C, theo_total))
                min_golden = max(0, theo_golden)
                max_golden = M - 1
            elif 0.5 <= D < 1.5 and G >= 1.5:  # case13
                min_total = N
                max_total = min(N + C, max(N + 1, int(round(N * (G ** 0.5)))))
                min_golden = M + 1
                max_golden = max(M + 1, min(M + C, theo_golden, max_total))
            elif 0.5 <= D < 1.5 and 0.5 <= G < 1.5:  # case14
                min_total = max(M, int(round(N * 0.8)))
                max_total = N + 1
                min_golden = max(1, M - 1)
                max_golden = M + 1
            elif 0.5 <= D < 1.5 and G < 0.5:  # case15
                min_total = max(1, int(round(N * 0.8)))
                max_total = N
                min_golden = max(0, theo_golden)
                max_golden = M - 1
            elif D < 0.5 and G >= 1.5:  # case16
                min_total = M
                max_total = N - 1
                min_golden = M
                max_golden = min(max_total, M + 1)
            elif D < 0.5 and 0.5 <= G < 1.5:  # case17
                min_total = max(M, theo_total)
                max_total = N - 1
                min_golden = max(1, M - 1)
                max_golden = M
            else:  # case18
                min_total = 0
                max_total = max(0, N - 1)
                min_golden = 0
                max_golden = max(0, M - 1)

    min_total = _clamp_int(min_total)
    max_total = _clamp_int(max_total)
    min_golden = _clamp_int(min_golden)
    max_golden = _clamp_int(max_golden)

    # 物理防穿透：最多总场 >= 最少总场；总场 >= 黄金场
    if min_total is not None and max_total is not None and max_total < min_total:
        max_total = min_total
    if min_total is not None and min_golden is not None and min_golden > min_total:
        min_golden = min_total
    if max_total is not None and max_golden is not None and max_golden > max_total:
        max_golden = max_total
    if min_golden is not None and max_golden is not None and max_golden < min_golden:
        max_golden = min_golden

    return min_total, max_total, min_golden, max_golden


def build_default_tuning_table(
    movies: List[Dict[str, Any]],
    movie_targets: Dict[str, Dict[str, Any]],
    today_eff: pd.DataFrame,
    next_day_schedule: List[Dict[str, Any]],
    box_office_data: List[Dict[str, Any]],
    efficiency_enabled: bool,
    rule12_enabled: bool,
    daily_delta_cap: int,
) -> pd.DataFrame:
    eff_map: Dict[str, Dict[str, Any]] = {}
    if not today_eff.empty:
        for _, row in today_eff.iterrows():
            eff_map[movie_policy_key(row.get("影片", ""))] = row.to_dict()

    bo_ranked = sort_movies_by_box_office(box_office_data)
    nextday_pre_top5 = {m for m, _ in bo_ranked[:5]} if rule12_enabled else set()

    today_rank = (
        today_eff.sort_values(["票房", "场次", "影片"], ascending=[False, False, True]).reset_index(drop=True)
        if (today_eff is not None and not today_eff.empty)
        else pd.DataFrame(columns=["影片", "票房", "场次"])
    )
    today_top10 = {movie_policy_key(x) for x in today_rank["影片"].head(10).tolist()}

    presale_rank = build_presale_rank_from_schedule(next_day_schedule)
    presale_movies = {m for m, _, t in presale_rank if t > 0}
    presale_top5 = {m for m, _, _ in presale_rank[:5]}

    rows: List[Dict[str, Any]] = []
    seen_policy: Set[str] = set()
    for m in movies:
        display_name = str(m.get("movieName") or "").strip()
        policy_key = movie_policy_key(display_name, m.get("movieMediaType", ""))
        if not display_name or not policy_key or policy_key in seen_policy:
            continue
        seen_policy.add(policy_key)

        mv = policy_key
        target = movie_targets.get(mv, {})
        eff = eff_map.get(mv, {})

        today_sessions = int(eff.get("场次", 0) or 0)
        today_golden_sessions = int(eff.get("黄金场次", 0) or 0)
        day_eff = round(float(eff.get("场次效率", 0) or 0), 3)
        golden_eff = round(float(eff.get("黄金效率", 0) or 0), 3)
        is_selected = (
            today_sessions > 0
            or mv in today_top10
            or mv in presale_top5
            or mv in presale_movies
            or mv in nextday_pre_top5
        )

        min_sessions: Optional[int] = None
        max_sessions: Optional[int] = None
        min_golden: Optional[int] = None
        max_golden: Optional[int] = None

        if is_selected and efficiency_enabled:
            if today_sessions <= 0:
                # 当日未上映：仅“今日票房Top10 / 次日预售Top5”默认允许 0~1
                if mv in today_top10 or mv in presale_top5 or mv in nextday_pre_top5:
                    min_sessions, max_sessions, min_golden, max_golden = 0, 1, 0, 1
                else:
                    min_sessions, max_sessions, min_golden, max_golden = None, None, None, None
            else:
                min_sessions, max_sessions, min_golden, max_golden = derive_efficiency_defaults_18_cases(
                    N=today_sessions,
                    M=today_golden_sessions,
                    D=day_eff,
                    G=golden_eff,
                    C=int(daily_delta_cap),
                )

            # 规则十二优先：票房Top5至少1个黄金场
            if mv in nextday_pre_top5:
                min_golden = max(1, int(min_golden or 0))
                min_sessions = max(1, int(min_sessions or 0))
                max_sessions = max(int(max_sessions or 0), min_sessions)
        else:
            min_sessions, max_sessions, min_golden, max_golden = None, None, None, None

        rows.append(
            {
                "选中": bool(is_selected),
                "影片": display_name,
                "今日场次": today_sessions,
                "今日黄金场次": today_golden_sessions,
                "今日全天效率": day_eff,
                "今日黄金效率": golden_eff,
                "最少场次": min_sessions,
                "最多场次": max_sessions,
                "固定场次": None,
                "最少黄金场次": min_golden,
                "最多黄金场次": max_golden,
                "最低场次占比": None,
                "最高场次占比": None,
            }
        )

    df = pd.DataFrame(rows)
    if df.empty:
        return df

    # 排序：当日影城票房降序，其次当日场次降序，再按影片名
    df["_sort_box"] = df["影片"].apply(lambda x: float((eff_map.get(movie_policy_key(x), {}) or {}).get("票房", 0) or 0))
    df["_sort_cnt"] = df["影片"].apply(lambda x: int((eff_map.get(movie_policy_key(x), {}) or {}).get("场次", 0) or 0))
    df = (
        df.sort_values(["_sort_box", "_sort_cnt", "影片"], ascending=[False, False, True])
        .drop(columns=["_sort_box", "_sort_cnt"])
        .reset_index(drop=True)
    )
    return coerce_tuning_editor_df(df)


def parse_movie_tuning_constraints(df: pd.DataFrame) -> Dict[str, Dict[str, Optional[float]]]:
    if df is None or df.empty:
        return {}

    def _num(v: Any) -> Optional[float]:
        if pd.isna(v) or v in ("", None):
            return None
        try:
            return float(v)
        except Exception:
            return None

    constraints: Dict[str, Dict[str, Optional[float]]] = {}
    for _, row in df.iterrows():
        selected = row.get("选中", True)
        if pd.notna(selected) and not bool(selected):
            continue

        mv = movie_policy_key(row.get("影片", ""))
        if not mv:
            continue

        fixed_sessions = _num(row.get("固定场次"))
        min_sessions = _num(row.get("最少场次"))
        max_sessions = _num(row.get("最多场次"))
        min_share_pct = _num(row.get("最低场次占比"))
        max_share_pct = _num(row.get("最高场次占比"))
        min_golden_sessions = _num(row.get("最少黄金场次"))
        max_golden_sessions = _num(row.get("最多黄金场次"))
        min_golden_ratio_pct = None
        max_golden_ratio_pct = None

        if min_share_pct is not None:
            min_share_pct = max(0.0, min(100.0, min_share_pct))
        if max_share_pct is not None:
            max_share_pct = max(0.0, min(100.0, max_share_pct))
        if min_golden_ratio_pct is not None:
            min_golden_ratio_pct = max(0.0, min(100.0, min_golden_ratio_pct))
        if max_golden_ratio_pct is not None:
            max_golden_ratio_pct = max(0.0, min(100.0, max_golden_ratio_pct))

        if fixed_sessions is not None:
            min_sessions = fixed_sessions
            max_sessions = fixed_sessions
        if min_sessions is not None and max_sessions is not None and min_sessions > max_sessions:
            max_sessions = min_sessions
        if min_golden_sessions is not None and max_golden_sessions is not None and min_golden_sessions > max_golden_sessions:
            max_golden_sessions = min_golden_sessions
        if min_sessions is not None and min_golden_sessions is not None:
            min_golden_sessions = min(min_golden_sessions, min_sessions)
        if max_sessions is not None and max_golden_sessions is not None:
            max_golden_sessions = min(max_golden_sessions, max_sessions)

        constraints[mv] = {
            "fixed_sessions": int(fixed_sessions) if fixed_sessions is not None else None,
            "min_sessions": int(min_sessions) if min_sessions is not None else None,
            "max_sessions": int(max_sessions) if max_sessions is not None else None,
            "min_share_pct": min_share_pct,
            "max_share_pct": max_share_pct,
            "min_golden_sessions": int(min_golden_sessions) if min_golden_sessions is not None else None,
            "max_golden_sessions": int(max_golden_sessions) if max_golden_sessions is not None else None,
            "min_golden_ratio_pct": min_golden_ratio_pct,
            "max_golden_ratio_pct": max_golden_ratio_pct,
        }
    return constraints


def coerce_tuning_editor_df(df: pd.DataFrame) -> pd.DataFrame:
    if df is None or df.empty:
        return pd.DataFrame()

    out = df.copy()
    int_cols = ["今日场次", "今日黄金场次", "最少场次", "最多场次", "固定场次", "最少黄金场次", "最多黄金场次"]
    float_cols = ["今日全天效率", "今日黄金效率", "最低场次占比", "最高场次占比"]

    for col in int_cols:
        if col in out.columns:
            out[col] = pd.to_numeric(out[col], errors="coerce").astype("Int64")
    for col in float_cols:
        if col in out.columns:
            out[col] = pd.to_numeric(out[col], errors="coerce").astype("Float64")
    if "选中" in out.columns:
        out["选中"] = out["选中"].fillna(False).astype(bool)
    if "影片" in out.columns:
        out["影片"] = out["影片"].astype(str).str.strip()
    return out


def build_candidate_summary_table(
    schedule: List[Dict[str, Any]],
    today_eff: pd.DataFrame,
    golden_start_dt: datetime,
    golden_end_dt: datetime,
) -> pd.DataFrame:
    if not schedule:
        return pd.DataFrame(
            columns=["影片", "场次", "今日黄金场次", "全天场次效率", "黄金时段场次效率", "次日场次", "次日黄金时段场次"]
        )

    df_next = pd.DataFrame(schedule)
    df_next["movieClean"] = df_next.apply(lambda r: movie_policy_key(r.get("movieName", ""), r.get("movieMediaType", "")), axis=1)
    next_total = df_next.groupby("movieClean").size().to_dict()
    next_golden = df_next[
        (df_next["startTime"] >= golden_start_dt) & (df_next["startTime"] <= golden_end_dt)
    ].groupby("movieClean").size().to_dict()

    rows: List[Dict[str, Any]] = []
    if today_eff is not None and not today_eff.empty:
        for _, row in today_eff.iterrows():
            mv = movie_policy_key(row.get("影片", ""))
            rows.append(
                {
                    "影片": mv,
                    "场次": int(row.get("场次", 0) or 0),
                    "今日黄金场次": int(row.get("黄金场次", 0) or 0),
                    "全天场次效率": float(row.get("场次效率", 0) or 0),
                    "黄金时段场次效率": float(row.get("黄金效率", 0) or 0),
                    "次日场次": int(next_total.get(mv, 0)),
                    "次日黄金时段场次": int(next_golden.get(mv, 0)),
                }
            )

    seen = {r["影片"] for r in rows}
    for mv, cnt in next_total.items():
        if mv in seen:
            continue
        rows.append(
            {
                "影片": mv,
                "场次": 0,
                "今日黄金场次": 0,
                "全天场次效率": 0.0,
                "黄金时段场次效率": 0.0,
                "次日场次": int(cnt),
                "次日黄金时段场次": int(next_golden.get(mv, 0)),
            }
        )

    out = pd.DataFrame(rows)
    if not out.empty:
        out = out.sort_values(["次日场次", "影片"], ascending=[False, True]).reset_index(drop=True)
    return out


def build_runtime_config_from_widgets(cfg: Dict[str, Any], widgets: Dict[str, Any]) -> Dict[str, Any]:
    rule2_ranges = [x.strip() for x in re.split(r"[\n,]", widgets["rule2_exempt_ranges"]) if x.strip()]
    forbidden_halls = [extract_hall_no(x.strip()) for x in re.split(r"[\n,]", widgets["rule13_forbidden_halls"]) if x.strip()]
    maintenance_blocks = normalize_maintenance_blocks_input(widgets["maintenance_blocks"])
    if widgets.get("maintenance_blocks") is None:
        maintenance_blocks = cfg.get("maintenance_blocks", [])

    runtime_cfg = {
        "business_start": hm_str(widgets["business_start"]),
        "business_end": hm_str(widgets["business_end"]),
        "turnaround_base": int(widgets["turnaround_base"]),
        "golden_start": hm_str(widgets["golden_start"]),
        "golden_end": hm_str(widgets["golden_end"]),
        "efficiency_enabled": bool(widgets["efficiency_enabled"]),
        "efficiency_penalty_coef": float(widgets["efficiency_penalty_coef"]),
        "eff_daily_delta_cap": int(widgets["eff_daily_delta_cap"]),
        "rule1_enabled": bool(widgets["rule1_enabled"]),
        "rule1_gap": int(widgets["rule1_gap"]),
        "rule2_enabled": bool(widgets["rule2_enabled"]),
        "rule2_threshold": int(widgets["rule2_threshold"]),
        "rule2_window_minutes": int(widgets["rule2_window_minutes"]),
        "rule2_penalty": float(widgets["rule2_penalty"]),
        "rule2_exempt_ranges": rule2_ranges,
        "rule3_enabled": bool(widgets["rule3_enabled"]),
        "rule3_gap_minutes": int(widgets["rule3_gap_minutes"]),
        "rule3_penalty": float(widgets["rule3_penalty"]),
        "rule4_enabled": bool(widgets["rule4_enabled"]),
        "rule4_earliest": hm_str(widgets["rule4_earliest"]),
        "rule4_latest": hm_str(widgets["rule4_latest"]),
        "rule9_enabled": bool(widgets["rule9_enabled"]),
        "rule9_hot_top_n": int(widgets["rule9_hot_top_n"]),
        "rule9_min_ratio": float(widgets["rule9_min_ratio"]),
        "rule9_penalty": float(widgets["rule9_penalty"]),
        "rule11_enabled": bool(widgets["rule11_enabled"]),
        "rule11_after_time": hm_str(widgets["rule11_after_time"]),
        "rule11_penalty": float(widgets["rule11_penalty"]),
        "rule12_enabled": bool(widgets["rule12_enabled"]),
        "rule12_penalty_each": float(widgets["rule12_penalty_each"]),
        "rule13_enabled": bool(widgets["rule13_enabled"]),
        "rule13_forbidden_halls": forbidden_halls,
        "tms_allowance": int(widgets["tms_allowance"]),
        "maintenance_blocks": maintenance_blocks,
        "iterations": int(widgets["iterations"]),
        "random_seed": int(widgets["random_seed"]),
    }

    merged = dict(DEFAULT_CONFIG)
    merged.update(runtime_cfg)
    return merged


def build_locked_movie_policy_set(locked_sessions: List[Dict[str, Any]]) -> Set[str]:
    out: Set[str] = set()
    for s in locked_sessions or []:
        mv = movie_policy_key(s.get("movieName", ""), s.get("movieMediaType", ""))
        if mv:
            out.add(mv)
    return out


def build_rule_context(
    bundle: Dict[str, Any],
    runtime_cfg: Dict[str, Any],
    manual_constraints: Dict[str, Dict[str, Optional[float]]],
    allowed_movies: Set[str],
) -> RuleContext:
    return RuleContext(
        target_date=bundle["target_date"],
        business_start_dt=bundle["biz_start_dt"],
        business_end_dt=bundle["biz_end_dt"],
        golden_start_dt=bundle["golden_start_dt"],
        golden_end_dt=bundle["golden_end_dt"],
        params=runtime_cfg,
        blockouts_by_hall=bundle["blockouts_by_hall"],
        movie_targets=bundle["movie_targets"],
        movie_weights=bundle["movie_weights"],
        tms_by_hall=bundle["tms_by_hall"],
        manual_constraints=manual_constraints,
        allowed_movies=allowed_movies,
        preview_windows_by_identity=bundle.get("preview_windows_by_identity", {}),
    )


def build_job_payload(
    *,
    bundle: Dict[str, Any],
    runtime_cfg: Dict[str, Any],
    manual_constraints: Dict[str, Dict[str, Optional[float]]],
    allowed_movies: Set[str],
) -> Dict[str, Any]:
    return {
        "target_str": bundle.get("target_str", ""),
        "runtime_cfg": dict(runtime_cfg),
        "manual_constraints": manual_constraints,
        "allowed_movies": set(allowed_movies),
        "bundle": {
            "target_date": bundle["target_date"],
            "target_str": bundle["target_str"],
            "hall_name_map": bundle["hall_name_map"],
            "locked_sessions": bundle["locked_sessions"],
            "movies": bundle["movies"],
            "preview_windows_by_identity": bundle.get("preview_windows_by_identity", {}),
            "blockouts_by_hall": bundle["blockouts_by_hall"],
            "biz_start_dt": bundle["biz_start_dt"],
            "biz_end_dt": bundle["biz_end_dt"],
            "golden_start_dt": bundle["golden_start_dt"],
            "golden_end_dt": bundle["golden_end_dt"],
            "today_eff": bundle["today_eff"],
            "movie_targets": bundle["movie_targets"],
            "movie_weights": bundle["movie_weights"],
            "box_office_data": bundle["box_office_data"],
            "tms_by_hall": bundle["tms_by_hall"],
        },
    }


def optimization_worker() -> None:
    try:
        payload = _read_pickle(JOB_PAYLOAD_FILE, {})
        if not isinstance(payload, dict) or not payload:
            write_job_state(status="failed", message="后台任务参数缺失", control="run")
            return

        bundle = payload.get("bundle") or {}
        runtime_cfg = payload.get("runtime_cfg") or dict(DEFAULT_CONFIG)
        manual_constraints = payload.get("manual_constraints") or {}
        allowed_movies = set(payload.get("allowed_movies") or [])
        target_str = str(payload.get("target_str") or "")
        iterations = int(runtime_cfg.get("iterations", 0) or 0)
        if iterations <= 0:
            write_job_state(status="failed", message="迭代次数必须大于0", control="run")
            return

        state0 = read_job_state()
        start_ts = float(state0.get("started_ts") or 0.0)
        if start_ts <= 0:
            start_ts = time.time()

        ctx = build_rule_context(bundle, runtime_cfg, manual_constraints, allowed_movies)
        feasible_map: Dict[str, CandidateResult] = {}
        hard_reject = 0
        build_reject = 0
        rule_reject = 0
        reject_reason_counter: Counter = Counter()
        reject_detail_counter: Counter = Counter()
        reject_phase_counter: Counter = Counter()
        reject_examples: Dict[str, List[str]] = defaultdict(list)
        last_push_ts = 0.0

        for i in range(iterations):
            state = read_job_state()
            control = str(state.get("control") or "run")
            elapsed_now = max(0.0, time.time() - start_ts)

            if control == "stop":
                write_job_state(
                    status="stopped",
                    message="任务已停止",
                    iter_done=i,
                    progress=round(i / iterations, 4),
                    feasible_count=len(feasible_map),
                    hard_reject=hard_reject,
                    build_reject=build_reject,
                    rule_reject=rule_reject,
                    reject_reason_top=dict(sorted(reject_reason_counter.items(), key=lambda x: x[1], reverse=True)[:10]),
                    reject_detail_top=dict(sorted(reject_detail_counter.items(), key=lambda x: x[1], reverse=True)[:10]),
                    elapsed_seconds=round(elapsed_now, 2),
                    ended_at=_now_text(),
                    result_count=0,
                    control="run",
                )
                return

            while control == "pause":
                write_job_state(
                    status="paused",
                    message="任务已暂停",
                    iter_done=i,
                    progress=round(i / iterations, 4),
                    feasible_count=len(feasible_map),
                    hard_reject=hard_reject,
                    build_reject=build_reject,
                    rule_reject=rule_reject,
                    reject_reason_top=dict(sorted(reject_reason_counter.items(), key=lambda x: x[1], reverse=True)[:10]),
                    reject_detail_top=dict(sorted(reject_detail_counter.items(), key=lambda x: x[1], reverse=True)[:10]),
                    elapsed_seconds=round(max(0.0, time.time() - start_ts), 2),
                )
                time.sleep(0.5)
                state = read_job_state()
                control = str(state.get("control") or "run")
                if control == "stop":
                    write_job_state(
                        status="stopped",
                        message="任务已停止",
                        iter_done=i,
                        progress=round(i / iterations, 4),
                        feasible_count=len(feasible_map),
                        hard_reject=hard_reject,
                        build_reject=build_reject,
                        rule_reject=rule_reject,
                        reject_reason_top=dict(sorted(reject_reason_counter.items(), key=lambda x: x[1], reverse=True)[:10]),
                        reject_detail_top=dict(sorted(reject_detail_counter.items(), key=lambda x: x[1], reverse=True)[:10]),
                        elapsed_seconds=round(max(0.0, time.time() - start_ts), 2),
                        ended_at=_now_text(),
                        result_count=0,
                        control="run",
                    )
                    return

            if runtime_cfg.get("random_seed", 0) > 0:
                random.seed(int(runtime_cfg["random_seed"]) + i)

            fail_reasons: List[str] = []
            sched = simulate_one_candidate(
                movies=bundle["movies"],
                hall_name_map=bundle["hall_name_map"],
                locked_sessions=bundle["locked_sessions"],
                ctx=ctx,
                fail_reason_out=fail_reasons,
            )

            if sched is None:
                hard_reject += 1
                build_reject += 1
                reject_phase_counter["构造阶段失败"] += 1
                reason = fail_reasons[0] if fail_reasons else "构造失败：随机构造阶段无可行候选"
                norm = normalize_reject_reason(reason)
                reject_reason_counter[norm] += 1
                reject_detail_counter[reason] += 1
                if len(reject_examples[norm]) < 5:
                    reject_examples[norm].append(reason)
            else:
                hard_violations = validate_hard_rules(sched, bundle["locked_sessions"], ctx)
                if hard_violations:
                    hard_reject += 1
                    rule_reject += 1
                    reject_phase_counter["硬性规则淘汰"] += 1
                    seen_reason: Set[str] = set()
                    for rv in hard_violations:
                        norm = normalize_reject_reason(rv)
                        if norm in seen_reason:
                            continue
                        seen_reason.add(norm)
                        reject_reason_counter[norm] += 1
                        reject_detail_counter[str(rv)] += 1
                        if len(reject_examples[norm]) < 5:
                            reject_examples[norm].append(str(rv))
                else:
                    cand = score_candidate(
                        schedule=sched,
                        ctx=ctx,
                        today_eff=bundle["today_eff"],
                        locked_sessions=bundle["locked_sessions"],
                        box_office_data=bundle["box_office_data"],
                    )
                    sig = schedule_signature(sched)
                    prev = feasible_map.get(sig)
                    if prev is None or cand.score > prev.score:
                        feasible_map[sig] = cand

            now_ts = time.time()
            if (i + 1 == iterations) or (i % 5 == 0) or (now_ts - last_push_ts >= 0.5):
                write_job_state(
                    status="running",
                    message=f"运行中：第 {i + 1}/{iterations} 轮，可行方案 {len(feasible_map)}",
                    iter_done=i + 1,
                    progress=round((i + 1) / iterations, 4),
                    feasible_count=len(feasible_map),
                    hard_reject=hard_reject,
                    build_reject=build_reject,
                    rule_reject=rule_reject,
                    reject_reason_top=dict(sorted(reject_reason_counter.items(), key=lambda x: x[1], reverse=True)[:10]),
                    reject_detail_top=dict(sorted(reject_detail_counter.items(), key=lambda x: x[1], reverse=True)[:10]),
                    elapsed_seconds=round(max(0.0, now_ts - start_ts), 2),
                )
                last_push_ts = now_ts

        elapsed_total = round(max(0.0, time.time() - start_ts), 2)
        reject_reason_stats = dict(sorted(reject_reason_counter.items(), key=lambda x: x[1], reverse=True))
        reject_detail_stats = dict(sorted(reject_detail_counter.items(), key=lambda x: x[1], reverse=True))
        reject_phase_stats = dict(reject_phase_counter)
        reject_examples_out = {k: v for k, v in reject_examples.items()}

        results = sorted(feasible_map.values(), key=lambda x: x.score, reverse=True)
        if not results:
            _atomic_write_pickle(
                JOB_RESULT_FILE,
                {
                    "results": [],
                    "all_results_count": 0,
                    "hard_reject": hard_reject,
                    "build_reject": build_reject,
                    "rule_reject": rule_reject,
                    "reject_reason_stats": reject_reason_stats,
                    "reject_detail_stats": reject_detail_stats,
                    "reject_phase_stats": reject_phase_stats,
                    "reject_examples": reject_examples_out,
                    "elapsed_seconds": elapsed_total,
                    "target_str": target_str,
                    "runtime_cfg": runtime_cfg,
                    "box_office_data": bundle.get("box_office_data", []),
                },
            )
            write_job_state(
                status="failed",
                message="未生成任何满足硬性约束的方案",
                iter_done=iterations,
                progress=1.0,
                feasible_count=0,
                hard_reject=hard_reject,
                build_reject=build_reject,
                rule_reject=rule_reject,
                reject_reason_top=dict(list(reject_reason_stats.items())[:10]),
                reject_detail_top=dict(list(reject_detail_stats.items())[:10]),
                elapsed_seconds=elapsed_total,
                ended_at=_now_text(),
                result_count=0,
                control="run",
            )
            return

        result_payload = {
            "results": [serialize_candidate(x) for x in results[:10]],
            "all_results_count": len(results),
            "hard_reject": hard_reject,
            "build_reject": build_reject,
            "rule_reject": rule_reject,
            "reject_reason_stats": reject_reason_stats,
            "reject_detail_stats": reject_detail_stats,
            "reject_phase_stats": reject_phase_stats,
            "reject_examples": reject_examples_out,
            "elapsed_seconds": elapsed_total,
            "target_str": target_str,
            "locked_count": len(bundle.get("locked_sessions", [])),
            "movie_targets": bundle.get("movie_targets", {}),
            "today_eff": bundle.get("today_eff", pd.DataFrame()),
            "golden_start_dt": bundle.get("golden_start_dt"),
            "golden_end_dt": bundle.get("golden_end_dt"),
            "box_office_data": bundle.get("box_office_data", []),
            "runtime_cfg": runtime_cfg,
        }
        _atomic_write_pickle(JOB_RESULT_FILE, result_payload)
        write_job_state(
            status="completed",
            message=f"完成：可行方案 {len(results)}，已保存 Top{min(10, len(results))}",
            iter_done=iterations,
            progress=1.0,
            feasible_count=len(results),
            hard_reject=hard_reject,
            build_reject=build_reject,
            rule_reject=rule_reject,
            reject_reason_top=dict(list(reject_reason_stats.items())[:10]),
            reject_detail_top=dict(list(reject_detail_stats.items())[:10]),
            elapsed_seconds=elapsed_total,
            ended_at=_now_text(),
            result_count=min(10, len(results)),
            control="run",
        )
    except Exception as e:
        state = read_job_state()
        start_ts = float(state.get("started_ts") or 0.0)
        elapsed = round(max(0.0, time.time() - start_ts), 2) if start_ts > 0 else 0.0
        write_job_state(
            status="failed",
            message=f"后台任务异常: {e}",
            elapsed_seconds=elapsed,
            ended_at=_now_text(),
            control="run",
        )


def start_background_job(payload: Dict[str, Any]) -> Tuple[bool, str]:
    global _JOB_THREAD
    worker = _find_live_worker()
    state = read_job_state()
    if worker is not None and state.get("status") in {"running", "paused"}:
        return False, "已有后台任务在运行，请先暂停/停止。"

    target_str = str(payload.get("target_str") or "")
    iterations = int((payload.get("runtime_cfg") or {}).get("iterations", 0) or 0)
    if iterations <= 0:
        return False, "迭代次数必须大于0。"

    _atomic_write_pickle(JOB_PAYLOAD_FILE, payload)
    job_id = str(int(time.time() * 1000))
    write_job_state(
        status="running",
        control="run",
        job_id=job_id,
        started_at=_now_text(),
        started_ts=time.time(),
        ended_at="",
        target_date=target_str,
        iterations=iterations,
        iter_done=0,
        progress=0.0,
        elapsed_seconds=0.0,
        feasible_count=0,
        hard_reject=0,
        build_reject=0,
        rule_reject=0,
        reject_reason_top={},
        reject_detail_top={},
        result_count=0,
        message="后台任务已启动",
    )
    _JOB_THREAD = threading.Thread(target=optimization_worker, name="nextday-opt-worker", daemon=True)
    _JOB_THREAD.start()
    return True, "后台任务已启动"


def main() -> None:
    st.title("🎬 次日排片最优化（随机贪心构造 + 蒙特卡洛评估）")
    st.caption(
        "按规则自动生成次日排片。硬性规则不满足直接淘汰，软性规则按评分排序，输出 Top10 方案和甘特图。"
    )

    cfg = load_config()

    c_date, c_target = st.columns([1, 2])
    with c_date:
        base_date = st.date_input("基准日期（默认今天）", value=date.today(), key="opt_base_date")
    with c_target:
        target_date = base_date + timedelta(days=1)
        st.info(f"将基于 **{base_date.strftime('%Y-%m-%d')}** 的规则与数据，生成 **{target_date.strftime('%Y-%m-%d')}** 的排片。")

    st.markdown("### 参数配置（全部在页面内展示，JSON 持久化）")

    col1, col2, col3 = st.columns(3)

    with col1:
        st.markdown("**营业与黄金时段**")
        w_business_start = st.time_input("营业开始", value=parse_hm(cfg["business_start"], "09:30"), key="w_business_start")
        w_business_end = st.time_input("营业结束", value=parse_hm(cfg["business_end"], "01:30"), key="w_business_end")
        w_turnaround_base = st.number_input("场次转换基准(分钟)", min_value=5, max_value=40, value=int(cfg["turnaround_base"]), step=1, key="w_turnaround_base")
        st.caption(f"硬约束转换范围：{max(1, int(w_turnaround_base)-3)} - {int(w_turnaround_base)+5} 分钟")
        w_golden_start = st.time_input("黄金时段开始", value=parse_hm(cfg["golden_start"], "14:00"), key="w_golden_start")
        w_golden_end = st.time_input("黄金时段结束", value=parse_hm(cfg["golden_end"], "21:00"), key="w_golden_end")
        w_efficiency_enabled = st.checkbox("启用排片效率分析表(18规则)", value=bool(cfg["efficiency_enabled"]), key="w_efficiency_enabled")
        w_efficiency_penalty_coef = st.number_input(
            "效率规则扣分系数",
            min_value=0.1,
            max_value=10.0,
            value=float(cfg.get("efficiency_penalty_coef", 1.0)),
            step=0.1,
            key="w_efficiency_penalty_coef",
            disabled=not w_efficiency_enabled,
        )
        w_eff_daily_delta_cap = st.number_input(
            "效率日增减上限 C",
            min_value=0,
            max_value=20,
            value=int(cfg.get("eff_daily_delta_cap", 5)),
            step=1,
            key="w_eff_daily_delta_cap",
            disabled=not w_efficiency_enabled,
        )

    with col2:
        st.markdown("**硬性规则参数**")
        w_rule1_enabled = st.checkbox("规则一：同影片最小开场间隔", value=bool(cfg["rule1_enabled"]), key="w_rule1_enabled")
        w_rule1_gap = st.number_input("规则一间隔(分钟)", min_value=5, max_value=180, value=int(cfg["rule1_gap"]), step=1, key="w_rule1_gap", disabled=not w_rule1_enabled)

        w_rule4_enabled = st.checkbox("规则四：首末场强约束", value=bool(cfg["rule4_enabled"]), key="w_rule4_enabled")
        w_rule4_earliest = st.time_input("最早场不得晚于", value=parse_hm(cfg["rule4_earliest"], "10:00"), key="w_rule4_earliest", disabled=not w_rule4_enabled)
        w_rule4_latest = st.time_input("最晚场不得早于", value=parse_hm(cfg["rule4_latest"], "22:30"), key="w_rule4_latest", disabled=not w_rule4_enabled)

        w_rule13_enabled = st.checkbox("规则十三：2/8/9号厅禁3D", value=bool(cfg["rule13_enabled"]), key="w_rule13_enabled")
        w_rule13_forbidden_halls = st.text_input(
            "禁3D影厅号(逗号分隔)",
            value=",".join([str(x) for x in cfg.get("rule13_forbidden_halls", ["2", "8", "9"])]),
            key="w_rule13_forbidden_halls",
            disabled=not w_rule13_enabled,
        )
        w_tms_allowance = st.number_input("TMS 缺片允许场次数", min_value=0, max_value=50, value=int(cfg["tms_allowance"]), step=1, key="w_tms_allowance")

    with col3:
        st.markdown("**软性规则参数**")
        w_rule2_enabled = st.checkbox("规则二：30分钟内开场超阈值", value=bool(cfg["rule2_enabled"]), key="w_rule2_enabled")
        w_rule2_threshold = st.number_input("规则二阈值(场)", min_value=1, max_value=20, value=int(cfg["rule2_threshold"]), step=1, key="w_rule2_threshold", disabled=not w_rule2_enabled)
        w_rule2_window_minutes = st.number_input("规则二窗口(分钟)", min_value=5, max_value=120, value=int(cfg["rule2_window_minutes"]), step=5, key="w_rule2_window_minutes", disabled=not w_rule2_enabled)
        w_rule2_penalty = st.number_input("规则二每超1场扣分", min_value=1.0, max_value=200.0, value=float(cfg["rule2_penalty"]), step=1.0, key="w_rule2_penalty", disabled=not w_rule2_enabled)

        w_rule3_enabled = st.checkbox("规则三：场次开场断档扣分", value=bool(cfg["rule3_enabled"]), key="w_rule3_enabled")
        w_rule3_gap_minutes = st.number_input("规则三断档阈值(分钟)", min_value=10, max_value=180, value=int(cfg["rule3_gap_minutes"]), step=5, key="w_rule3_gap_minutes", disabled=not w_rule3_enabled)
        w_rule3_penalty = st.number_input("规则三扣分系数", min_value=1.0, max_value=100.0, value=float(cfg["rule3_penalty"]), step=1.0, key="w_rule3_penalty", disabled=not w_rule3_enabled)

        w_rule9_enabled = st.checkbox("规则九：黄金热门密度", value=bool(cfg["rule9_enabled"]), key="w_rule9_enabled")
        w_rule9_hot_top_n = st.number_input("规则九热门TopN", min_value=1, max_value=10, value=int(cfg["rule9_hot_top_n"]), step=1, key="w_rule9_hot_top_n", disabled=not w_rule9_enabled)
        w_rule9_min_ratio = st.slider("规则九最小占比", min_value=0.05, max_value=0.90, value=float(cfg["rule9_min_ratio"]), step=0.05, key="w_rule9_min_ratio", disabled=not w_rule9_enabled)
        w_rule9_penalty = st.number_input("规则九扣分", min_value=1.0, max_value=200.0, value=float(cfg["rule9_penalty"]), step=1.0, key="w_rule9_penalty", disabled=not w_rule9_enabled)

    st.markdown("**规则二豁免时段（可多个，逗号或换行分隔）**")
    w_rule2_exempt_ranges = st.text_area(
        "例如：14:00-15:00, 19:00-20:00",
        value=", ".join(cfg.get("rule2_exempt_ranges", ["14:00-15:00", "19:00-20:00"])),
        height=70,
        key="w_rule2_exempt_ranges",
    )

    c4, c5, c6 = st.columns(3)
    with c4:
        w_rule11_enabled = st.checkbox("规则十一：22:00后热门影片", value=bool(cfg["rule11_enabled"]), key="w_rule11_enabled")
        w_rule11_after_time = st.time_input("规则十一起算时间", value=parse_hm(cfg["rule11_after_time"], "22:00"), key="w_rule11_after_time", disabled=not w_rule11_enabled)
        w_rule11_penalty = st.number_input("规则十一扣分", min_value=1.0, max_value=200.0, value=float(cfg["rule11_penalty"]), step=1.0, key="w_rule11_penalty", disabled=not w_rule11_enabled)

    with c5:
        w_rule12_enabled = st.checkbox("规则十二：票房Top5需黄金场", value=bool(cfg["rule12_enabled"]), key="w_rule12_enabled")
        w_rule12_penalty_each = st.number_input("规则十二每片扣分", min_value=1.0, max_value=200.0, value=float(cfg["rule12_penalty_each"]), step=1.0, key="w_rule12_penalty_each", disabled=not w_rule12_enabled)
        w_iterations = st.number_input("Monte Carlo迭代次数", min_value=20, max_value=100000, value=int(cfg["iterations"]), step=10, key="w_iterations")

    with c6:
        w_random_seed = st.number_input("随机种子(可复现)", min_value=0, max_value=99999999, value=int(cfg["random_seed"]), step=1, key="w_random_seed")

    st.markdown("**影厅维护/包场时段（可直接增删行，更便捷）**")
    maintenance_df_default = pd.DataFrame(
        cfg.get("maintenance_blocks", []),
        columns=["hall", "start", "end"],
    )
    w_maintenance_blocks = st.data_editor(
        maintenance_df_default,
        num_rows="dynamic",
        use_container_width=True,
        hide_index=True,
        key="w_maintenance_blocks_editor",
        column_config={
            "hall": st.column_config.TextColumn("影厅", help="例如 2号厅 / 2"),
            "start": st.column_config.TextColumn("开始", help="HH:MM"),
            "end": st.column_config.TextColumn("结束", help="HH:MM"),
        },
    )

    runtime_cfg = build_runtime_config_from_widgets(
        cfg,
        {
            "business_start": w_business_start,
            "business_end": w_business_end,
            "turnaround_base": w_turnaround_base,
            "golden_start": w_golden_start,
            "golden_end": w_golden_end,
            "efficiency_enabled": w_efficiency_enabled,
            "efficiency_penalty_coef": w_efficiency_penalty_coef,
            "eff_daily_delta_cap": w_eff_daily_delta_cap,
            "rule1_enabled": w_rule1_enabled,
            "rule1_gap": w_rule1_gap,
            "rule2_enabled": w_rule2_enabled,
            "rule2_threshold": w_rule2_threshold,
            "rule2_window_minutes": w_rule2_window_minutes,
            "rule2_penalty": w_rule2_penalty,
            "rule2_exempt_ranges": w_rule2_exempt_ranges,
            "rule3_enabled": w_rule3_enabled,
            "rule3_gap_minutes": w_rule3_gap_minutes,
            "rule3_penalty": w_rule3_penalty,
            "rule4_enabled": w_rule4_enabled,
            "rule4_earliest": w_rule4_earliest,
            "rule4_latest": w_rule4_latest,
            "rule9_enabled": w_rule9_enabled,
            "rule9_hot_top_n": w_rule9_hot_top_n,
            "rule9_min_ratio": w_rule9_min_ratio,
            "rule9_penalty": w_rule9_penalty,
            "rule11_enabled": w_rule11_enabled,
            "rule11_after_time": w_rule11_after_time,
            "rule11_penalty": w_rule11_penalty,
            "rule12_enabled": w_rule12_enabled,
            "rule12_penalty_each": w_rule12_penalty_each,
            "rule13_enabled": w_rule13_enabled,
            "rule13_forbidden_halls": w_rule13_forbidden_halls,
            "tms_allowance": w_tms_allowance,
            "maintenance_blocks": w_maintenance_blocks,
            "iterations": w_iterations,
            "random_seed": w_random_seed,
        },
    )

    b1, b2 = st.columns([1, 5])
    with b1:
        if st.button("↩ 恢复默认参数", use_container_width=True):
            save_config(dict(DEFAULT_CONFIG))
            st.rerun()

    save_config(runtime_cfg)
    st.caption(f"参数已自动保存到 {CONFIG_FILE}（日期参数不写入配置）")

    st.divider()

    target_str = target_date.strftime("%Y-%m-%d")
    today_str = base_date.strftime("%Y-%m-%d")

    load_btn = st.button("📥 加载数据并生成可编辑微调约束", type="primary", use_container_width=True)
    if load_btn:
        with st.status("正在加载数据...", expanded=True) as status:
            status.write("1/7 拉取次日/当日排片、影厅座位")
            next_day_schedule, hall_seat_map, err_next = fetch_schedule_and_halls(target_str)
            today_schedule, _, err_today = fetch_schedule_and_halls(today_str)

            if err_next:
                status.update(label="失败：次日排片接口异常", state="error")
                st.error(f"次日排片拉取失败：{err_next}")
                st.stop()
            if err_today:
                status.update(label="失败：当日排片接口异常", state="error")
                st.error(f"当日排片拉取失败：{err_today}")
                st.stop()

            hall_name_map = build_hall_name_map(next_day_schedule, hall_seat_map)
            locked_sessions = build_locked_sessions(next_day_schedule, target_date)

            status.write("2/7 拉取次日可放映电影")
            movies = fetch_movie_info_for_date(target_str)
            if not movies:
                status.update(label="失败：未获取到可放映电影", state="error")
                st.error("getMovieInfo 接口未返回可放映电影，无法生成排片。")
                st.stop()
            movies = dedupe_movies_by_policy_key(movies)
            preview_windows_by_identity = build_preview_windows_for_movies(target_date, movies)

            status.write("3/7 构建包场/维护窗口、黄金时段、营业时间")
            blockouts = parse_blockouts_from_config(target_date, runtime_cfg.get("maintenance_blocks", []))
            blockouts_by_hall = build_hall_blockouts(blockouts, hall_name_map)

            biz_start_t = parse_hm(runtime_cfg["business_start"], "09:30")
            biz_end_t = parse_hm(runtime_cfg["business_end"], "01:30")
            golden_start_t = parse_hm(runtime_cfg["golden_start"], "14:00")
            golden_end_t = parse_hm(runtime_cfg["golden_end"], "21:00")

            biz_start_dt = parse_operating_dt(target_date, biz_start_t)
            biz_end_dt = parse_operating_dt(target_date, biz_end_t)
            if biz_end_dt <= biz_start_dt:
                biz_end_dt += timedelta(days=1)

            golden_start_dt = parse_operating_dt(target_date, golden_start_t)
            golden_end_dt = parse_operating_dt(target_date, golden_end_t)
            if golden_end_dt < golden_start_dt:
                golden_end_dt += timedelta(days=1)

            status.write("4/7 拉取票房Top与TMS信息")
            box_office_data = fetch_realtime_box_office(target_str)
            if not box_office_data:
                box_office_data = fetch_realtime_box_office(today_str)
            tms_rows = fetch_tms_server_movies_raw()
            tms_by_hall = build_tms_index_by_hall(tms_rows)

            status.write("5/7 生成可放映电影微调约束（可编辑）")
            today_eff = build_today_efficiency(today_schedule, hall_seat_map, golden_start_t, golden_end_t)
            movie_targets = build_movie_targets(
                movies=movies,
                today_eff=today_eff,
                locked_sessions=locked_sessions,
                box_office_data=box_office_data,
                rule12_enabled=bool(runtime_cfg["rule12_enabled"]),
            )
            movie_weights = build_movie_weights(movies, movie_targets, box_office_data)
            tuning_df = build_default_tuning_table(
                movies=movies,
                movie_targets=movie_targets,
                today_eff=today_eff,
                next_day_schedule=next_day_schedule,
                box_office_data=box_office_data,
                efficiency_enabled=bool(runtime_cfg["efficiency_enabled"]),
                rule12_enabled=bool(runtime_cfg["rule12_enabled"]),
                daily_delta_cap=int(runtime_cfg.get("eff_daily_delta_cap", 5)),
            )

            st.session_state["nextday_loaded_bundle"] = {
                "target_date": target_date,
                "target_str": target_str,
                "today_str": today_str,
                "next_day_schedule": next_day_schedule,
                "today_schedule_raw": list(today_schedule),
                "today_schedule_excluded_labels": [],
                "today_schedule": today_schedule,
                "hall_seat_map": hall_seat_map,
                "hall_name_map": hall_name_map,
                "locked_sessions": locked_sessions,
                "movies": movies,
                "preview_windows_by_identity": preview_windows_by_identity,
                "blockouts_by_hall": blockouts_by_hall,
                "biz_start_dt": biz_start_dt,
                "biz_end_dt": biz_end_dt,
                "golden_start_dt": golden_start_dt,
                "golden_end_dt": golden_end_dt,
                "today_eff": today_eff,
                "movie_targets": movie_targets,
                "movie_weights": movie_weights,
                "box_office_data": box_office_data,
                "tms_by_hall": tms_by_hall,
            }
            st.session_state["nextday_tuning_df"] = coerce_tuning_editor_df(tuning_df)
            st.session_state.pop("nextday_tuning_editor", None)
            status.update(label="完成：已加载数据并生成微调约束", state="complete")

    bundle = st.session_state.get("nextday_loaded_bundle")
    job_state = read_job_state()
    if job_state.get("status") in {"running", "paused"} and _find_live_worker() is None:
        if int(job_state.get("iter_done") or 0) < int(job_state.get("iterations") or 0):
            job_state = write_job_state(status="failed", control="run", message="后台任务已中断，请重新启动。")
    job_result = _read_pickle(JOB_RESULT_FILE, {})

    if bundle:
        if bundle.get("target_str") != target_str:
            st.warning("你已切换日期，请重新点击“加载数据并生成可编辑微调约束”。")
        else:
            raw_today_schedule = bundle.get("today_schedule_raw") or bundle.get("today_schedule") or []
            excluded_default = bundle.get("today_schedule_excluded_labels", [])
            exclude_options = [session_display_label(s) for s in raw_today_schedule]
            exclude_options = sorted(list(dict.fromkeys(exclude_options)))

            with st.expander("🗑️ 剔除特殊场次 (包场/无效数据)", expanded=False):
                exclude_key = f"nextday_today_exclude_{target_str}"
                selected_labels = st.multiselect(
                    "选择需要剔除的当日场次（仅影响效率分析与默认微调建议）",
                    options=exclude_options,
                    default=[x for x in excluded_default if x in exclude_options],
                    key=exclude_key,
                )

                if sorted(selected_labels) != sorted(excluded_default):
                    filtered_today_schedule = apply_session_exclusions(raw_today_schedule, selected_labels)
                    golden_start_t = parse_hm(runtime_cfg["golden_start"], "14:00")
                    golden_end_t = parse_hm(runtime_cfg["golden_end"], "21:00")
                    today_eff = build_today_efficiency(
                        filtered_today_schedule,
                        bundle["hall_seat_map"],
                        golden_start_t,
                        golden_end_t,
                    )
                    movie_targets = build_movie_targets(
                        movies=bundle["movies"],
                        today_eff=today_eff,
                        locked_sessions=bundle["locked_sessions"],
                        box_office_data=bundle["box_office_data"],
                        rule12_enabled=bool(runtime_cfg["rule12_enabled"]),
                    )
                    movie_weights = build_movie_weights(bundle["movies"], movie_targets, bundle["box_office_data"])
                    tuning_df = build_default_tuning_table(
                        movies=bundle["movies"],
                        movie_targets=movie_targets,
                        today_eff=today_eff,
                        next_day_schedule=bundle["next_day_schedule"],
                        box_office_data=bundle["box_office_data"],
                        efficiency_enabled=bool(runtime_cfg["efficiency_enabled"]),
                        rule12_enabled=bool(runtime_cfg["rule12_enabled"]),
                        daily_delta_cap=int(runtime_cfg.get("eff_daily_delta_cap", 5)),
                    )
                    bundle["today_schedule_excluded_labels"] = selected_labels
                    bundle["today_schedule"] = filtered_today_schedule
                    bundle["today_eff"] = today_eff
                    bundle["movie_targets"] = movie_targets
                    bundle["movie_weights"] = movie_weights
                    st.session_state["nextday_loaded_bundle"] = bundle
                    st.session_state["nextday_tuning_df"] = coerce_tuning_editor_df(tuning_df)
                    st.session_state.pop("nextday_tuning_editor", None)
                    st.rerun()

            st.markdown("### 微调约束（留空则不生效）")
            st.caption("可按影片设置：最多/最少/固定场次、场次占比范围、黄金场次范围。仅勾选行参与约束。")
            tune_df_base = st.session_state.get("nextday_tuning_df", pd.DataFrame())
            editor_cache = st.session_state.get("nextday_tuning_editor")
            if isinstance(editor_cache, pd.DataFrame) and isinstance(tune_df_base, pd.DataFrame):
                same_shape = (
                    list(editor_cache.columns) == list(tune_df_base.columns)
                    and len(editor_cache) == len(tune_df_base)
                )
                tune_df = editor_cache.copy() if same_shape else tune_df_base.copy()
            else:
                tune_df = tune_df_base.copy() if isinstance(tune_df_base, pd.DataFrame) else pd.DataFrame()
            if tune_df.empty and isinstance(tune_df_base, pd.DataFrame):
                tune_df = tune_df_base.copy()

            try:
                film_col = st.column_config.TextColumn("影片", pinned="left")
            except TypeError:
                film_col = st.column_config.TextColumn("影片")
            edited_tune_df = st.data_editor(
                tune_df,
                num_rows="fixed",
                use_container_width=True,
                hide_index=True,
                disabled=["影片", "今日场次", "今日黄金场次", "今日全天效率", "今日黄金效率"],
                key="nextday_tuning_editor",
                column_order=[
                    "选中",
                    "影片",
                    "今日场次",
                    "今日黄金场次",
                    "今日全天效率",
                    "今日黄金效率",
                    "最少场次",
                    "最多场次",
                    "固定场次",
                    "最少黄金场次",
                    "最多黄金场次",
                    "最低场次占比",
                    "最高场次占比",
                ],
                column_config={
                    "选中": st.column_config.CheckboxColumn("选中", default=False),
                    "影片": film_col,
                    "今日场次": st.column_config.NumberColumn("今日场次"),
                    "今日黄金场次": st.column_config.NumberColumn("今日黄金场次"),
                    "今日全天效率": st.column_config.NumberColumn("今日全天效率", format="%.3f"),
                    "今日黄金效率": st.column_config.NumberColumn("今日黄金效率", format="%.3f"),
                    "固定场次": st.column_config.NumberColumn("固定场次", min_value=0, step=1),
                    "最少场次": st.column_config.NumberColumn("最少场次", min_value=0, step=1),
                    "最多场次": st.column_config.NumberColumn("最多场次", min_value=0, step=1),
                    "最少黄金场次": st.column_config.NumberColumn("最少黄金场次", min_value=0, step=1),
                    "最多黄金场次": st.column_config.NumberColumn("最多黄金场次", min_value=0, step=1),
                    "最低场次占比": st.column_config.NumberColumn("最低场次占比", min_value=0.0, max_value=100.0, step=0.5),
                    "最高场次占比": st.column_config.NumberColumn("最高场次占比", min_value=0.0, max_value=100.0, step=0.5),
                },
            )
            # 直接保存编辑器结果，避免每轮强制类型转换导致“需编辑两次”现象
            st.session_state["nextday_tuning_df"] = edited_tune_df.copy()

            st.markdown("### 后台运算控制")
            st.caption("支持暂停、继续、停止；刷新页面后任务仍可持续运行并展示实时进度。")

            state_target = str(job_state.get("target_date") or "")
            state_status = str(job_state.get("status") or "idle")
            progress_ratio = float(job_state.get("progress") or 0.0)
            progress_ratio = max(0.0, min(1.0, progress_ratio))
            iter_done = int(job_state.get("iter_done") or 0)
            iterations = int(job_state.get("iterations") or 0)
            feasible_count = int(job_state.get("feasible_count") or 0)
            hard_reject = int(job_state.get("hard_reject") or 0)
            build_reject = int(job_state.get("build_reject") or 0)
            rule_reject = int(job_state.get("rule_reject") or 0)
            elapsed_seconds = float(job_state.get("elapsed_seconds") or 0.0)
            reject_reason_top = job_state.get("reject_reason_top") or {}
            reject_detail_top = job_state.get("reject_detail_top") or {}
            status_msg = str(job_state.get("message") or "")

            s1, s2, s3, s4, s5 = st.columns(5)
            s1.metric("任务状态", state_status)
            s2.metric("当前进度", f"{progress_ratio * 100:.1f}%")
            s3.metric("可行方案", feasible_count)
            s4.metric("硬性淘汰", hard_reject)
            s5.metric("已运行时长", f"{elapsed_seconds:.1f}s")
            st.progress(progress_ratio)
            if iterations > 0:
                st.caption(
                    f"迭代进度：{iter_done}/{iterations}；构造失败 {build_reject}，硬规则淘汰 {rule_reject}；{status_msg}"
                )
            elif status_msg:
                st.caption(status_msg)

            if reject_reason_top:
                reason_df = pd.DataFrame(
                    [{"淘汰原因": k, "次数": int(v)} for k, v in reject_reason_top.items()]
                )
                st.dataframe(reason_df, use_container_width=True, hide_index=True, height=220)
            if reject_detail_top:
                detail_df = pd.DataFrame(
                    [{"详细原因": k, "次数": int(v)} for k, v in reject_detail_top.items()]
                )
                st.dataframe(detail_df, use_container_width=True, hide_index=True, height=220)

            c_run, c_pause, c_resume, c_stop = st.columns(4)
            run_disabled = state_status in {"running", "paused"}
            do_run = c_run.button("🚀 后台生成 Top10", type="primary", use_container_width=True, disabled=run_disabled)
            do_pause = c_pause.button("⏸ 暂停", use_container_width=True, disabled=state_status != "running")
            do_resume = c_resume.button("▶️ 继续", use_container_width=True, disabled=state_status != "paused")
            do_stop = c_stop.button("⏹ 停止", use_container_width=True, disabled=state_status not in {"running", "paused"})

            if do_pause:
                write_job_state(control="pause", message="收到暂停请求")
                st.rerun()
            if do_resume:
                write_job_state(control="run", message="任务继续", status="running")
                st.rerun()
            if do_stop:
                write_job_state(control="stop", message="收到停止请求")
                st.rerun()

            if do_run:
                manual_constraints = parse_movie_tuning_constraints(edited_tune_df)
                allowed_movies = extract_allowed_movies_from_tuning_df(edited_tune_df)
                # 预售锁定优先：即使未勾选，也必须允许保留已售锁定场次
                allowed_movies |= build_locked_movie_policy_set(bundle.get("locked_sessions", []))
                payload = build_job_payload(
                    bundle=bundle,
                    runtime_cfg=runtime_cfg,
                    manual_constraints=manual_constraints,
                    allowed_movies=allowed_movies,
                )
                ok, msg = start_background_job(payload)
                if ok:
                    st.session_state["nextday_results"] = []
                    st.success(msg)
                    st.rerun()
                st.warning(msg)

            # 任务运行时自动刷新，展示实时进度
            if state_target == target_str and state_status == "running":
                time.sleep(0.8)
                st.rerun()

    if not bundle and job_state.get("status") in {"running", "paused", "completed", "failed", "stopped"}:
        st.markdown("### 后台任务状态")
        progress_ratio = float(job_state.get("progress") or 0.0)
        progress_ratio = max(0.0, min(1.0, progress_ratio))
        iter_done = int(job_state.get("iter_done") or 0)
        iterations = int(job_state.get("iterations") or 0)
        feasible_count = int(job_state.get("feasible_count") or 0)
        hard_reject = int(job_state.get("hard_reject") or 0)
        build_reject = int(job_state.get("build_reject") or 0)
        rule_reject = int(job_state.get("rule_reject") or 0)
        elapsed_seconds = float(job_state.get("elapsed_seconds") or 0.0)
        reject_reason_top = job_state.get("reject_reason_top") or {}
        reject_detail_top = job_state.get("reject_detail_top") or {}
        status_msg = str(job_state.get("message") or "")
        state_status = str(job_state.get("status") or "idle")
        state_target = str(job_state.get("target_date") or "")

        q1, q2, q3, q4, q5 = st.columns(5)
        q1.metric("任务状态", state_status)
        q2.metric("目标日期", state_target or "-")
        q3.metric("可行方案", feasible_count)
        q4.metric("硬性淘汰", hard_reject)
        q5.metric("已运行时长", f"{elapsed_seconds:.1f}s")
        st.progress(progress_ratio)
        if iterations > 0:
            st.caption(
                f"迭代进度：{iter_done}/{iterations}；构造失败 {build_reject}，硬规则淘汰 {rule_reject}；{status_msg}"
            )
        elif status_msg:
            st.caption(status_msg)

        if reject_reason_top:
            reason_df = pd.DataFrame(
                [{"淘汰原因": k, "次数": int(v)} for k, v in reject_reason_top.items()]
            )
            st.dataframe(reason_df, use_container_width=True, hide_index=True, height=220)
        if reject_detail_top:
            detail_df = pd.DataFrame(
                [{"详细原因": k, "次数": int(v)} for k, v in reject_detail_top.items()]
            )
            st.dataframe(detail_df, use_container_width=True, hide_index=True, height=220)

        f_pause, f_resume, f_stop = st.columns(3)
        if f_pause.button("⏸ 暂停任务", use_container_width=True, disabled=state_status != "running"):
            write_job_state(control="pause", message="收到暂停请求")
            st.rerun()
        if f_resume.button("▶️ 继续任务", use_container_width=True, disabled=state_status != "paused"):
            write_job_state(control="run", status="running", message="任务继续")
            st.rerun()
        if f_stop.button("⏹ 停止任务", use_container_width=True, disabled=state_status not in {"running", "paused"}):
            write_job_state(control="stop", message="收到停止请求")
            st.rerun()

        if state_status == "running":
            time.sleep(0.8)
            st.rerun()

    # 完成态：从后台结果文件恢复展示数据
    if isinstance(job_result, dict) and job_result.get("target_str") == target_str and job_state.get("status") == "completed":
        raw_rs = list(job_result.get("results") or [])
        st.session_state["nextday_results"] = [x for x in (deserialize_candidate(r) for r in raw_rs) if x is not None]
        st.session_state["nextday_all_results"] = int(job_result.get("all_results_count", 0))
        st.session_state["nextday_hard_reject"] = int(job_result.get("hard_reject", 0))
        st.session_state["nextday_build_reject"] = int(job_result.get("build_reject", 0))
        st.session_state["nextday_rule_reject"] = int(job_result.get("rule_reject", 0))
        st.session_state["nextday_elapsed_seconds"] = float(job_result.get("elapsed_seconds", 0.0))
        st.session_state["nextday_reject_reason_stats"] = job_result.get("reject_reason_stats", {})
        st.session_state["nextday_reject_detail_stats"] = job_result.get("reject_detail_stats", {})
        st.session_state["nextday_reject_phase_stats"] = job_result.get("reject_phase_stats", {})
        st.session_state["nextday_reject_examples"] = job_result.get("reject_examples", {})
        st.session_state["nextday_target_date"] = str(job_result.get("target_str", ""))
        st.session_state["nextday_locked_count"] = int(job_result.get("locked_count", 0))
        st.session_state["nextday_movie_targets"] = job_result.get("movie_targets", {})
        st.session_state["nextday_today_eff"] = job_result.get("today_eff", pd.DataFrame())
        st.session_state["nextday_golden_start_dt"] = job_result.get("golden_start_dt")
        st.session_state["nextday_golden_end_dt"] = job_result.get("golden_end_dt")
        st.session_state["nextday_runtime_cfg"] = job_result.get("runtime_cfg", runtime_cfg)
        st.session_state["nextday_box_office_data"] = job_result.get("box_office_data", [])

    if (
        isinstance(job_result, dict)
        and job_result.get("target_str") == target_str
        and job_state.get("status") in {"failed", "stopped"}
        and job_result.get("reject_reason_stats")
    ):
        st.markdown("### 最近一次任务淘汰统计")
        st.caption(f"运行耗时：{float(job_result.get('elapsed_seconds', 0.0)):.1f}s")
        p1, p2 = st.columns(2)
        p1.metric("构造阶段失败", int(job_result.get("build_reject", 0)))
        p2.metric("硬性规则淘汰", int(job_result.get("rule_reject", 0)))
        reason_df = pd.DataFrame(
            [{"淘汰原因": k, "次数": int(v)} for k, v in (job_result.get("reject_reason_stats") or {}).items()]
        ).sort_values("次数", ascending=False)
        st.dataframe(reason_df, use_container_width=True, hide_index=True, height=280)
        if job_result.get("reject_detail_stats"):
            detail_df = pd.DataFrame(
                [{"详细原因": k, "次数": int(v)} for k, v in (job_result.get("reject_detail_stats") or {}).items()]
            ).sort_values("次数", ascending=False)
            st.dataframe(detail_df, use_container_width=True, hide_index=True, height=280)

    raw_results = st.session_state.get("nextday_results", [])
    results: List[CandidateResult] = [x for x in (deserialize_candidate(r) for r in (raw_results or [])) if x is not None]
    st.session_state["nextday_results"] = results
    if results:
        st.divider()
        target_str = st.session_state.get("nextday_target_date", "")
        total_feasible = st.session_state.get("nextday_all_results", 0)
        hard_reject = st.session_state.get("nextday_hard_reject", 0)
        build_reject = st.session_state.get("nextday_build_reject", 0)
        rule_reject = st.session_state.get("nextday_rule_reject", 0)
        elapsed_seconds = st.session_state.get("nextday_elapsed_seconds", 0.0)
        locked_count = st.session_state.get("nextday_locked_count", 0)
        display_runtime_cfg = st.session_state.get("nextday_runtime_cfg", runtime_cfg)
        display_box_office_data = st.session_state.get("nextday_box_office_data", [])
        reject_reason_stats = st.session_state.get("nextday_reject_reason_stats", {})
        reject_detail_stats = st.session_state.get("nextday_reject_detail_stats", {})
        reject_phase_stats = st.session_state.get("nextday_reject_phase_stats", {})
        reject_examples = st.session_state.get("nextday_reject_examples", {})

        m1, m2, m3, m4, m5 = st.columns(5)
        m1.metric("目标排片日期", target_str)
        m2.metric("可行方案数", total_feasible)
        m3.metric("硬性规则淘汰", hard_reject)
        m4.metric("已售锁定场", locked_count)
        m5.metric("生成耗时", f"{float(elapsed_seconds):.1f}s")

        with st.expander("硬性淘汰统计", expanded=False):
            c_stat1, c_stat2 = st.columns(2)
            c_stat1.metric("构造阶段失败", int(build_reject))
            c_stat2.metric("硬性规则淘汰", int(rule_reject))

            if reject_phase_stats:
                phase_df = pd.DataFrame(
                    [{"淘汰阶段": k, "次数": int(v)} for k, v in reject_phase_stats.items()]
                ).sort_values("次数", ascending=False)
                st.dataframe(phase_df, use_container_width=True, hide_index=True)

            if reject_reason_stats:
                reason_df = pd.DataFrame(
                    [{"淘汰原因": k, "次数": int(v)} for k, v in reject_reason_stats.items()]
                ).sort_values("次数", ascending=False)
                st.dataframe(reason_df, use_container_width=True, hide_index=True, height=300)

            if reject_detail_stats:
                detail_df = pd.DataFrame(
                    [{"详细原因": k, "次数": int(v)} for k, v in reject_detail_stats.items()]
                ).sort_values("次数", ascending=False)
                st.dataframe(detail_df, use_container_width=True, hide_index=True, height=300)

            if reject_examples:
                show_examples: List[str] = []
                for reason, samples in reject_examples.items():
                    if not samples:
                        continue
                    show_examples.append(f"{reason}:")
                    for x in samples[:3]:
                        show_examples.append(f"- {x}")
                if show_examples:
                    st.code("\n".join(show_examples), language="text")

        with st.expander("影片目标(由效率规则推导)", expanded=False):
            targets = st.session_state.get("nextday_movie_targets", {})
            st.json(targets)

        with st.expander("当日效率基准", expanded=False):
            today_eff = st.session_state.get("nextday_today_eff")
            if isinstance(today_eff, pd.DataFrame) and not today_eff.empty:
                st.dataframe(today_eff, use_container_width=True, hide_index=True)
            else:
                st.info("无当日效率数据")

        tabs = st.tabs([f"方案{i+1}｜分数 {results[i].score:.1f}" for i in range(len(results))])
        for i, tab in enumerate(tabs):
            with tab:
                cand = results[i]
                c1, c2 = st.columns([2, 1])

                with c1:
                    render_gantt(cand.schedule, target_str, tab_key=f"{i}")

                with c2:
                    st.caption("评分拆解")
                    bd_df = pd.DataFrame(cand.score_breakdown, columns=["规则", "分值", "说明"])
                    st.dataframe(bd_df, use_container_width=True, hide_index=True, height=280)

                st.caption("结果汇总")
                today_eff = st.session_state.get("nextday_today_eff")
                g_st = st.session_state.get("nextday_golden_start_dt")
                g_et = st.session_state.get("nextday_golden_end_dt")
                summary_df = build_candidate_summary_table(cand.schedule, today_eff, g_st, g_et)
                st.dataframe(
                    summary_df[["影片", "场次", "今日黄金场次", "全天场次效率", "黄金时段场次效率", "次日场次", "次日黄金时段场次"]],
                    use_container_width=True,
                    hide_index=True,
                    height=340,
                )

                st.markdown("#### 🔍 场次合理性检查日志")
                log_target_date = datetime.strptime(target_str, "%Y-%m-%d").date()
                log_text = generate_schedule_check_logs_text(
                    schedule=cand.schedule,
                    target_date=log_target_date,
                    params=display_runtime_cfg,
                    today_eff=today_eff if isinstance(today_eff, pd.DataFrame) else pd.DataFrame(),
                    box_office_data=display_box_office_data,
                )
                st.code(log_text, language="text")


if __name__ == "__main__":
    main()