project/tbsg/mian2.py

"""
饿了么闪购 — 主入口  V1
步骤驱动，按步执行
"""
import sys
import time
import re
import json
import cv2
import numpy as np
from pathlib import Path
from typing import Optional

sys.path.insert(0, str(Path(__file__).parent / "steps"))
from ocr import OCR
from executor import SafeExecutor
from ai_helper1 import AIParser, parse_instructions, extract_value_after, detect_popup
from ai_helper_vision import VisionParser
from db import save_record, get_existing_license
from snapshot import collect_snapshot

# ── 配置 ────────────────────────────────────────────────
APP_PACKAGE = "me.ele"
SCREENSHOT_DIR = Path(__file__).parent / "screenshots"

# 生僻字标题修正开关：
#   True  = 用云端文本修正标题中的生僻字漏字（如"理王"→"理洫王"）
#   False = 标题保持OCR原样（默认，更安全——避免形近字误修正，如"温胃舒"被改成"养胃舒"导致错误采集）
ENABLE_RARE_CHAR_CORRECT = True

# 标题过滤开关：
#   True  = 按品牌/药品名/规格过滤，只采目标商品（正常模式）
#   False = 不过滤，直接采集OCR识别的全部结果入库（测试OCR准确率用）
ENABLE_TITLE_FILTER = False

# 视觉识别开关：
#   True  = 截图直接送视觉大模型识别（方案A，生僻字更准，坐标精度依赖模型）
#   False = 本地OCR + 文本AI解析（原有方案）
USE_VISION_PARSER = True

# 验证码频率控制：
#   一天内 ≥CAPTCHA_DAILY_LIMIT 次 → 立即停止采集并回告（风控可能封号）
#   每累计 CAPTCHA_REST_EVERY 次 → 休息 CAPTCHA_REST_MINUTES 分钟（第2/4/6次休息，第8次直接停止）
CAPTCHA_DAILY_LIMIT = 8
CAPTCHA_REST_EVERY = 2
CAPTCHA_REST_MINUTES = 30
CAPTCHA_LOG_DIR = Path(__file__).parent / "logs"   # 验证码日志目录（每设备独立一个文件，独立计数）
CAPTCHA_ABORTED = False  # 全局标志：验证码频繁触发停止采集（调度上报用）
CAPTCHA_ABORT_REASON = ""  # 停止原因（调度回告用）
ACCOUNT_ABORTED = False  # 全局标志：账号被踢/封号，停止采集（调度上报用）
CURRENT_PAGE = 0  # 当前页码（逐页回告/终止回告用，调度侧存续采集进度）
# 验证码判定截图存档目录（每次判定出现验证码时截图保存，人工确认是否误判）
CAPTCHA_CHECK_DIR = Path(__file__).parent / "logs" / "captcha_check"

# 休息期间进度回告用的全局上下文（step3 任务开始时设置，_captcha_rest 休息时每10分钟回告）
_SCHEDULER = None
_TASK_ID = None
_CRAWLED_COUNT = 0

OCR = OCR()

# 说明书打叉坐标缓存（仅本任务内有效）：任务中第一个商品模板匹配找到打叉后，
# 本任务后续商品直接复用该坐标（说明书页布局固定，同设备位置不变）；
# 新任务开始时清空，重新匹配。
_CLOSE_BTN_CACHE = None


def _find_device(device_id: str = "") -> str:
    import subprocess
    r = subprocess.run(["adb", "devices"], capture_output=True, text=True, timeout=5)
    devices = []
    for line in r.stdout.strip().split("\n")[1:]:
        if line.strip() and "device" in line and "offline" not in line:
            s = line.split("\t")[0].strip()
            if s:
                devices.append(s)

    if not devices:
        raise RuntimeError("未找到设备")

    # 如果指定了设备ID，精确匹配
    if device_id:
        for d in devices:
            if d == device_id:
                return d
        raise RuntimeError(f"未找到指定设备: {device_id}，可用设备: {devices}")

    # 只有一台直接返回
    if len(devices) == 1:
        return devices[0]

    # 多台设备：列出并让用户选择
    print(f"\n发现 {len(devices)} 台设备:")
    for i, d in enumerate(devices):
        print(f"  [{i}] {d}")
    while True:
        try:
            choice = input(f"请选择设备 [0-{len(devices)-1}]，回车默认第一台: ").strip()
            if choice == "":
                return devices[0]
            idx = int(choice)
            if 0 <= idx < len(devices):
                return devices[idx]
        except ValueError:
            pass
        print(f"输入无效，请输入 0-{len(devices)-1}")


def _find_text_in_area(shot_path: str, target: str, max_y: int) -> Optional[dict]:
    results = OCR.recognize(shot_path, detail="all")
    for r in results:
        if target in r["text"]:
            y = r["bbox"][0][1]
            if y < max_y:
                cx = r["bbox"][0][0] + (r["bbox"][2][0] - r["bbox"][0][0]) // 2
                cy = y + (r["bbox"][2][1] - y) // 2
                return {"x": cx, "y": cy, "text": r["text"], "conf": r["confidence"]}
    return None


def _shot_path(ex: SafeExecutor, name: str) -> str:
    """截图路径：按 设备ID/步骤 分类组织目录（screenshots/设备/step1/xxx.png）"""
    cat = "misc"
    for prefix, c in (("step1", "step1"), ("step2", "step2"), ("step3", "step3"),
                      ("_s4_", "step4"), ("sort", "step2"), ("inst_", "instructions"),
                      ("lic_", "license"), ("snap_", "snapshot"), ("ad_", "popup")):
        if name.startswith(prefix):
            cat = c
            break
    d = SCREENSHOT_DIR / ex.device_id / cat
    d.mkdir(parents=True, exist_ok=True)
    return str(d / name)


def _screenshot(ex: SafeExecutor, name: str) -> str:
    import os
    # 按 设备ID/步骤 隔离截图（目录已含设备ID，文件名不再加设备前缀）
    path = _shot_path(ex, name)
    if os.path.exists(path):
        # 保留历史截图副本：固定文件名被 test/调试脚本引用，不能被后续运行覆盖丢失
        import shutil
        stem, ext = os.path.splitext(name)
        backup = str(Path(path).parent / f"{stem}_{int(time.time() * 1000)}{ext}")
        try:
            shutil.copy2(path, backup)
        except Exception:
            pass
        # 自动清理：每个固定文件只保留最近3份备份，超出删除最旧的
        try:
            olds = sorted(
                Path(path).parent.glob(f"{stem}_[0-9]*{ext}"),
                key=lambda p: p.stat().st_mtime, reverse=True,
            )
            for p in olds[3:]:
                p.unlink(missing_ok=True)
        except Exception:
            pass
    # 方案2：截图后验证完整性（adb 流式传输可能中断，导致 PNG 损坏）
    for _try in range(3):
        ex.driver.screenshot(path)
        if cv2.imread(path) is not None:
            break
        time.sleep(0.5)
    return path


def _where_am_i(ex: SafeExecutor) -> str:
    """判断当前位置：list=搜索结果列表页 home=首页 other=其他（全屏OCR，区分列表页与首页防止退过头）"""
    import os as _os
    tmp = _shot_path(ex, "check_pos.png")
    ex.driver.screenshot(tmp)
    if cv2.imread(tmp) is None:
        ex.driver.screenshot(tmp)
    texts = [r["text"] for r in OCR.recognize(tmp, detail="all")]
    if any("筛选" in t for t in texts):
        return "list"
    if any("看病买药" in t for t in texts) and any("我的" in t for t in texts):
        return "home"
    return "other"


def _is_search_page(ex: SafeExecutor) -> bool:
    """判断当前是否在搜索页面：只检测屏幕顶部20%区域内是否有「筛选」"""
    import tempfile, os
    w, h = ex.driver.window_size()
    tmp = _shot_path(ex, "check_search.png")
    ex.driver.screenshot(tmp)
    if cv2.imread(tmp) is None:
        ex.driver.screenshot(tmp)
    texts = OCR.recognize(tmp, rect=[0, 0, w, int(h * 0.2)], detail="text")
    return "筛选" in texts


# ── 步骤 1：打开 App ────────────────────────────────────

def step1_open_app(ex: SafeExecutor) -> bool:
    print("=" * 40)
    print("  步骤 1：打开饿了么闪购")
    print("=" * 40)
    w, h = ex.driver.window_size()
    print(f"[step1] 屏幕尺寸: {w}x{h}")

    print(f"[step1] 关闭 {APP_PACKAGE}...")
    ex.driver.app_stop(APP_PACKAGE)
    time.sleep(2)

    print(f"[step1] 启动 {APP_PACKAGE}...")
    ex.driver.app_start(APP_PACKAGE)
    time.sleep(5)

    # 等待首页加载：底部导航「我的」出现（元素没加载完就多等重试，不一次定生死）
    for attempt in range(3):
        shot = _screenshot(ex, "step1_home.png")
        texts = OCR.recognize(shot, rect=[0, int(h * 0.88), w, h], detail="text")
        print(f"[step1] 底部识别(第{attempt+1}次): {texts}")
        if any("我的" in t for t in texts):
            print("[step1] OK - 成功进入 App")
            _close_ad_popup(ex)  # 每天首启的广告弹窗
            return True
        time.sleep(3)
    print("[step1] FAIL - 多次重试未检测到「我的」")
    # 识别不到"我的"可能因为账号被踢/封号——检测登录页xpath确认（存在才是封号）
    _check_account_kicked(ex)
    return False


# ── 步骤 2：搜索商品 ────────────────────────────────────

def _click_low_price_sort(ex: SafeExecutor) -> bool:
    """
    搜索结果排序：点「综合」→ 选「低价优先」（失败不阻塞，找不到就跳过）。
    找不到「低价优先」时再点一次「综合」重试，仍找不到则关闭排序弹窗。
    """
    try:
        # 1. 找「综合」并点击（排序入口，结果页顶部）
        shot = _shot_path(ex, "sort1.png")
        ex.driver.screenshot(shot)
        btn = None
        for r in OCR.recognize(shot, detail="all"):
            if "综合" in r["text"]:
                box = r["bbox"]
                btn = {"x": (box[0][0] + box[2][0]) // 2, "y": (box[0][1] + box[2][1]) // 2}
                break
        if not btn:
            print("    ⚠ 未找到「综合」排序入口，跳过排序")
            return False
        print(f"    排序: 点击「综合」({btn['x']}, {btn['y']})")
        ex.tap(btn["x"], btn["y"])
        time.sleep(2)

        # 2. 找「低价优先」
        shot2 = _shot_path(ex, "sort2.png")
        ex.driver.screenshot(shot2)
        low = None
        for r in OCR.recognize(shot2, detail="all"):
            if "低价优先" in r["text"]:
                box = r["bbox"]
                low = {"x": (box[0][0] + box[2][0]) // 2, "y": (box[0][1] + box[2][1]) // 2}
                break
        if low:
            print(f"    排序: 点击「低价优先」({low['x']}, {low['y']})")
            ex.tap(low["x"], low["y"])
            time.sleep(3)
            return True

        # 3. 没找到：再点一次「综合」关闭排序弹窗（点击综合弹出面板，再点一次收起）
        print("    ⚠ 未找到「低价优先」，再点一次「综合」关闭排序弹窗")
        ex.tap(btn["x"], btn["y"])
        time.sleep(1.5)
        return False
    except Exception as e:
        print(f"    ⚠ 排序异常: {e}")
        return False


def step2_search(ex: SafeExecutor, keyword: str) -> bool:
    print("\n" + "=" * 40)
    print(f"  步骤 2：搜索「{keyword}」")
    print("=" * 40)
    w, h = ex.driver.window_size()
    top_th = int(h * 0.3)

    # ── 阶段0+1：点击「看病买药」→ 找「搜索」（失败重试3次，3次失败重启App重试，最多2轮）──
    btn = None
    for round_idx in range(2):
        if round_idx > 0:
            # 第2轮：重启 App 重新进入（弹窗/页面异常时清状态）
            print("[step2] 重启App重新进入...")
            if not step1_open_app(ex):
                print("[step2] FAIL - 重启App失败")
                return False
        # 阶段0：点击「看病买药」
        shot0 = _screenshot(ex, "step2_phase0.png")
        btn_med = _find_text_in_area(shot0, "看病买药", h)
        if not btn_med:
            print(f"[step2] 未找到「看病买药」(第{round_idx+1}轮)")
            continue
        print(f"[step2] 找到「看病买药」: ({btn_med['x']}, {btn_med['y']})")
        ex.tap(btn_med["x"], btn_med["y"])
        time.sleep(5)
        _close_ad_popup(ex)  # 点击买药后可能出现的广告弹窗

        # 阶段1：找「搜索」（重试3次，每次关弹窗+等待）
        for attempt in range(3):
            shot = _screenshot(ex, "step2_phase1.png")
            btn = _find_text_in_area(shot, "搜索", top_th)
            if btn:
                break
            print(f"[step2] 未找到「搜索」(第{attempt+1}次)，关闭弹窗后重试")
            _close_ad_popup(ex)
            time.sleep(2)
        if btn:
            break  # 找到搜索，继续
        print("[step2] 3次未找到「搜索」，准备重启App重试")
    if not btn:
        print("[step2] FAIL - 多次重试+重启后仍未找到「搜索」")
        return False
    print(f"[step2] 找到「搜索」: ({btn['x']}, {btn['y']})")

    cx = btn["x"] - 300  # 搜索左边约120px
    cy = btn["y"]
    print(f"[step2] 点击搜索栏: ({cx}, {cy})")
    ex.tap(cx, cy)
    time.sleep(5)

    # 搜索页确认：「搜索」位置变化才认为进入（加载慢/弹窗遮挡/点击未生效就重试）
    moved = False
    btn2 = None
    for attempt in range(3):
        shot2 = _screenshot(ex, "step2_phase2.png")
        btn2 = _find_text_in_area(shot2, "搜索", top_th)
        if btn2 and (abs(btn2["x"] - btn["x"]) > 50 or abs(btn2["y"] - btn["y"]) > 50):
            moved = True
            print(f"[step2] 搜索页搜索: ({btn2['x']}, {btn2['y']})")
            break
        # 位置没变：先关广告弹窗 + AI验证码检测，再重新点击搜索栏（可能点击没生效或被验证码拦截）
        if _ai_check_captcha(ex):
            print(f"[step2] AI检测到验证码(第{attempt+1}次)，处理完成")
        if _close_ad_popup(ex):
            print(f"[step2] 已关闭广告弹窗(第{attempt+1}次)")
        print(f"[step2] 搜索位置未变化(第{attempt+1}次)，重新点击搜索栏")
        ex.tap(cx, cy)
        time.sleep(4)
    if not btn2:
        print("[step2] FAIL - 进入搜索页后找不到「搜索」")
        return False
    if not moved:
        print("[step2] FAIL - 搜索位置未改变")
        return False

    cx2 = btn2["x"] - 180  # 搜索页输入框在搜索左边约180px
    cy2 = btn2["y"]
    ex.tap(cx2, cy2)
    time.sleep(2)
    print(f"[step2] 聚焦输入框，等待2s")

    print(f"[step2] 输入关键词: {keyword}")
    ex.driver.set_input_ime(True)
    time.sleep(0.3)
    ex.driver.send_keys(keyword)
    time.sleep(1)

    ex.tap(btn2["x"], btn2["y"])
    time.sleep(3)

    # 搜索后检测列表页：先处理验证码（可能挡住列表页），再检测列表页特征（加载慢就重试）
    for attempt in range(3):
        shot3 = _screenshot(ex, "step2_result.png")
        raw3 = OCR.recognize(shot3, detail="all")
        all_texts = [r["text"] for r in raw3]

        # 1. 验证码优先：验证码弹窗会挡住列表页特征，先处理再重新检测
        #    关键词没命中时用AI再判断一次（九宫格/点击式验证码文字不在关键词里）
        if any(("拖动滑块" in t) or ("请按住滑块" in t) or ("安全验证" in t) for t in all_texts):
            print(f"[step2] 检测到列表页验证码(第{attempt+1}次)，尝试处理...")
            if _handle_captcha(ex, all_texts):
                print("[step2] 验证码已解决")
            else:
                print("[step2] 验证码处理失败")
                return False
            # 验证码通过后可能出现「出错了/检修中」页，点重新加载
            if _click_reload(ex):
                print("[step2] 已点击重新加载")
            time.sleep(2)
            continue
        if _ai_check_captcha(ex):
            print(f"[step2] AI检测到验证码(第{attempt+1}次)，处理完成")
            continue

        # 1.2 广告弹窗（进入列表页时也可能弹出，挡住列表页特征）
        if _close_ad_popup(ex):
            print(f"[step2] 已关闭列表页广告弹窗(第{attempt+1}次)")
            continue

        # 1.5 出错/检修页（无验证码时也可能出现）：点「重新加载」后重新检测
        if any("重新加载" in t for t in all_texts):
            print(f"[step2] 检测到「出错了/检修中」页面，点击重新加载")
            _click_reload(ex)
            continue

        # 2. 列表页特征检测
        has_filter = "筛选" in all_texts
        has_express = "快递" in all_texts
        kw_found = any(keyword in t for t in all_texts)
        print(f"[step2] 有筛选: {has_filter}, 有快递: {has_express}, 关键词存在: {kw_found}")

        if has_filter or has_express:
            # 如果有「快递」则点击它
            if has_express:
                for r in raw3:
                    if "快递" in r["text"]:
                        bx = r["bbox"]
                        cx = (bx[0][0] + bx[2][0]) // 2
                        cy = (bx[0][1] + bx[2][1]) // 2
                        print(f"[step2] 点击「快递」: ({cx}, {cy})")
                        ex.tap(cx, cy)
                        time.sleep(3)
                        break
                # 点击「快递」后设置排序：综合 → 低价优先
                # _click_low_price_sort(ex)
            print("[step2] OK - 搜索成功")
            return True

        print(f"[step2] 列表页特征未出现(第{attempt+1}次)，等3秒重试")
        time.sleep(3)

    print("[step2] FAIL - 搜索未成功")
    return False


def normalize_match_text(value):
    """归一化：统一全角/半角、去除空白和零宽字符（美团同款，避免'看起来一样但匹配失败'）"""
    import unicodedata
    text = "" if value is None else str(value)
    text = unicodedata.normalize("NFKC", text)
    text = re.sub(r"[\s ​-‍]+", "", text)
    return text


def _edit_distance(a: str, b: str) -> int:
    """编辑距离（Levenshtein），用于OCR错字容错"""
    if len(a) < len(b):
        a, b = b, a
    if not b:
        return len(a)
    prev = list(range(len(b) + 1))
    for i, ca in enumerate(a, 1):
        cur = [i]
        for j, cb in enumerate(b, 1):
            cur.append(min(prev[j] + 1, cur[j - 1] + 1, prev[j - 1] + (ca != cb)))
        prev = cur
    return prev[-1]


def _match_fuzzy(target: str, text: str) -> bool:
    """
    容错匹配：目标词是否在文本中（处理OCR漏字/错字1个）。
    用于判断「疑似生僻字」——本地识别差1个字时触发云端验证。
    """
    if not target:
        return True
    if target in text:
        return True
    if len(target) >= 3:
        for i in range(len(target)):  # 漏字：目标删任意1字后匹配
            if target[:i] + target[i + 1:] in text:
                return True
        for L in (len(target), len(target) + 1, len(target) - 1):  # 错字：编辑距离<=1
            if L < 2:
                continue
            for i in range(len(text) - L + 1):
                if _edit_distance(target, text[i:i + L]) <= 1:
                    return True
    return False


def _correct_title_with_cloud(title: str, n_brand: str, n_key: str, cloud_texts: list) -> str:
    """
    用云端文本修正标题中的生僻字错误（精确子串替换，不做整标题替换）：
    标题里 fuzzy 匹配到目标词的子串 → 替换为云端文本确认的准确形式。

    只处理"漏字"场景（云端形式更长，如"理王"→"理洫王"）；
    同长度的形近替换（如"温胃舒"→"养胃舒"）【不修正】——
    云端文本里的目标词可能来自其他商品，误用来改标题会造成错误采集。
    """
    c_joined = "".join(cloud_texts)
    nt = normalize_match_text(title)
    for target in (n_brand, n_key):
        if not target:
            continue
        if target in nt:
            continue  # 已精确匹配，无需修正
        if target not in c_joined:
            continue  # 云端也没有准确形式 → 无法修正，跳过
        # 在标题里找漏字子串并替换为准确形式。
        # 只处理"目标词比子串长且编辑距离≤1"（漏字：理王→理洫王）——
        # 同长度形近替换（温胃舒→养胃舒）不触发：云端文本里的目标词可能来自其他商品，
        # 误用来改标题会造成错误采集。
        for i in range(len(nt)):
            for L in (len(target) - 1, len(target), len(target) + 1):
                if L < 2 or i + L > len(nt):
                    continue
                sub = nt[i:i + L]
                if len(target) > len(sub) and _edit_distance(target, sub) <= 1:
                    nt = nt[:i] + target + nt[i + L:]
                    break
            else:
                continue
            break
    return nt


def _is_cjk(ch: str) -> bool:
    """是否中文字符"""
    return '一' <= ch <= '鿿'


def _is_missing_char(target: str, title_text: str) -> bool:
    """
    判断目标词是否在标题里以"漏1字"形式出现（生僻字特征）：
    目标词删掉1个字后的子串，在标题里是【独立词】（前后不是汉字）。
    例：理洫王删"洫"="理王"，标题"[理王]"里独立 → 漏字 ✓
    例：养胃舒删"舒"="养胃"，标题"滋阴养胃"里嵌在词中（前有"滋"）→ 非独立 → 不算漏字 ✗
    """
    for k in range(len(target)):
        sub = target[:k] + target[k + 1:]
        if len(sub) < 2:
            continue
        idx = title_text.find(sub)
        while idx != -1:
            before_ok = idx == 0 or not _is_cjk(title_text[idx - 1])
            after_ok = idx + len(sub) >= len(title_text) or not _is_cjk(title_text[idx + len(sub)])
            if before_ok and after_ok:
                return True
            idx = title_text.find(sub, idx + 1)
    return False


def _ensure_cloud(cloud_cache: dict, shot_path: str) -> None:
    """触发一次云端OCR（每批只调一次），保存 (文本, y) 带坐标的块列表"""
    if cloud_cache["done"]:
        return
    cloud_cache["done"] = True
    try:
        raw_c = OCR.recognize(shot_path, detail="all", engine="cloud")
        if not raw_c:
            # 云端返回空（配额用尽/限流时百度返回错误码但不抛异常）→ 视为云端不可用
            print("[step3] 云端返回空结果（可能配额不足/限流），按容错处理")
            cloud_cache["blocks"] = None
            cloud_cache["texts"] = None
            return
        cloud_cache["blocks"] = [
            (normalize_match_text(r["text"]), (r["bbox"][0][1] + r["bbox"][2][1]) // 2)
            for r in raw_c
        ]
        cloud_cache["texts"] = [t for t, _ in cloud_cache["blocks"]]
        print(f"[step3] 云端二次确认（{len(cloud_cache['blocks'])}块）")
    except Exception as e:
        print(f"[step3] 云端识别失败({e})")
        cloud_cache["blocks"] = None
        cloud_cache["texts"] = None


def _cloud_region_text(cloud_cache: dict, card_y: int, max_dy: int = 300) -> Optional[str]:
    """
    取卡片 y±max_dy 内的所有云端块合并文本（标题块可能被拆成多块，只取最近一块会漏）。
    阈值300px：排除相邻卡片的目标词（温胃舒 y=867 vs 养胃舒 y=1305 差438 > 300 ✓）
    """
    blocks = cloud_cache.get("blocks")
    if not blocks:
        return None
    parts = [t for t, cy in blocks if abs(cy - card_y) <= max_dy]
    return "".join(parts) if parts else None


def _cloud_crop_confirm(shot_path: str, card_y: int) -> Optional[str]:
    """
    用本地OCR的标题块坐标裁剪标题区域（标题y-20 ~ y+80，右列），放大2倍后云端识别。
    裁剪区只含当前商品的标题 → 云端不需要返回坐标，标准版（无配额问题）即可用，且字放大识别更准。
    """
    try:
        img = cv2.imread(shot_path)
        if img is None:
            return None
        h, w = img.shape[:2]
        y1, y2 = max(0, card_y - 20), min(h, card_y + 80)
        x1 = int(w * 0.15)
        crop = img[y1:y2, x1:w]
        if crop.size == 0:
            return None
        big = cv2.resize(crop, None, fx=2, fy=2, interpolation=cv2.INTER_LANCZOS4)
        raw = OCR.recognize(big, detail="all", engine="cloud")
        texts = [normalize_match_text(r["text"]) for r in raw
                 if any('一' <= c <= '鿿' for c in r["text"])]
        return "".join(texts) if texts else None
    except Exception as e:
        print(f"    [调试] 裁剪云端确认异常: {e}")
        return None


def _match_verify(title: str, n_brand: str, n_key: str, shot_path: str, cloud_cache: dict,
                  card_y: int = None) -> tuple:
    """
    品牌/药品名匹配（只判断品牌+药品名核心词，规格/功效文字不参与）。
    返回 (判定, 不匹配原因)：判定 "ok"/"fuzzy"/"fail"，原因如 "品牌"/"药品名"/"品牌、药品名"
    - 本地精确匹配 → "ok"（免费）
    - 完全不像 → "fail"（免费，不花云端）
    - 差1字/漏字 → 用本地坐标裁剪该商品标题区域，云端识别确认：
        裁剪区含目标词（本地认错字，如理王→理洫王/甲疏咪唑）→ "ok"
        裁剪区不含目标词（确实不是该商品，如温胃舒vs养胃舒）→ "fail"
    - 裁剪确认不可用 → 漏字场景容错放行 "fuzzy"，换字场景 "fail"
    """
    nt = normalize_match_text(title)
    brand_match = (not n_brand) or (n_brand in nt)
    key_match = (not n_key) or (n_key in nt)
    if brand_match and key_match:
        return "ok", ""

    def _unmatched_reason():
        parts = []
        if n_brand and not brand_match:
            parts.append("品牌")
        if n_key and not key_match:
            parts.append("药品名")
        return "、".join(parts) or "品牌/药品名"

    # 便宜判断：完全不像（非差1字也非漏字）→ 直接过滤，不花云端
    near_brand = (not n_brand) or _match_fuzzy(n_brand, nt) or _is_missing_char(n_brand, nt)
    near_key = (not n_key) or _match_fuzzy(n_key, nt) or _is_missing_char(n_key, nt)
    if not (near_brand and near_key):
        return "fail", _unmatched_reason()

    # 差1字/漏字 → 用本地坐标裁剪该商品标题区域，云端确认（标准版即可，无需坐标）
    if card_y is not None:
        crop_text = _cloud_crop_confirm(shot_path, card_y)
        if crop_text is not None:
            if (not n_brand or n_brand in crop_text) and (not n_key or n_key in crop_text):
                # 把裁剪确认文本交给标题修正逻辑（如"理王"→"理洫王"）
                cloud_cache["texts"] = [crop_text]
                return "ok", ""
            print(f"    [调试] 卡片y={card_y} 裁剪云端=[{crop_text[:40]}] 不含目标词")
            return "fail", _unmatched_reason()
        print(f"    [调试] 卡片y={card_y} 裁剪云端识别失败")

    # 裁剪确认不可用 → 原逻辑：漏字（独立词）→ 整页云端兜底；换字 → 过滤
    missing = []
    if n_brand and not brand_match and _is_missing_char(n_brand, nt):
        missing.append("品牌")
    if n_key and not key_match and _is_missing_char(n_key, nt):
        missing.append("药品名")
    if not missing:
        return "fail", _unmatched_reason()  # 换字 → 过滤（安全默认）

    # 漏字 → 整页云端兜底（cloud_cache 保证每批只调一次）
    _ensure_cloud(cloud_cache, shot_path)
    if cloud_cache["texts"] is None:
        return "fuzzy", ""
    c_joined = "".join(cloud_cache["texts"])
    if (not n_brand or n_brand in c_joined) and (not n_key or n_key in c_joined):
        return "ok", ""
    return "fail", _unmatched_reason()


def _screen_state(texts: list) -> str:
    """
    根据 OCR 文本判断屏幕状态（统一的状态识别，各步骤共用）：
    detail=商品详情页  shop=店铺页  list=搜索结果列表页  unknown=其他
    """
    joined = "".join(texts)
    if any(("加入购物车" in t) or ("立即购买" in t) or ("选规格" in t) or ("加入购物袋" in t) for t in texts):
        return "detail"
    if ("刚刚搜过" in joined) and ("评价" in joined):
        return "shop"
    if "筛选" in joined:
        return "list"
    return "unknown"


def _is_list_page(ex: SafeExecutor) -> bool:
    """当前是否在搜索结果列表页（全屏检测「筛选」，店铺页/详情页不含此词）"""
    import os as _os
    tmp = _shot_path(ex, "check_list.png")
    ex.driver.screenshot(tmp)
    if cv2.imread(tmp) is None:
        ex.driver.screenshot(tmp)
    texts = [r["text"] for r in OCR.recognize(tmp, detail="all")]
    return any("筛选" in t for t in texts)


def _find_ad_close(shot_path: str) -> Optional[dict]:
    """
    广告弹窗打叉按钮：二值化模板匹配，形状匹配不受颜色/背景干扰。
    双模板双二值化（固定阈值100 + OTSU自适应，各自同阈值组合），不同弹窗打叉深浅不同，取最佳。
    命中返回 {"x","y"}。
    """
    base_dir = Path(__file__).parent / "files"
    screen = cv2.imread(shot_path, cv2.IMREAD_GRAYSCALE)
    if screen is None:
        return None
    h_s, w_s = screen.shape[:2]
    base_w = 1220.0  # 模板裁自1220宽屏，其他分辨率按比例缩放
    scale_ratio = w_s / base_w
    scales = [round(scale_ratio * s, 2) for s in [0.8, 0.9, 1.0, 1.1, 1.2]]
    best_val, best_loc, best_sw, best_sh = 0, None, 0, 0
    # (模板文件, 截图二值化方式)：同阈值组合（固定×固定、OTSU×OTSU）
    pairs = (
        ("ad_close_bin.png", lambda g: cv2.threshold(g, 100, 255, cv2.THRESH_BINARY_INV)[1]),
        ("ad_close_otsu.png", lambda g: cv2.threshold(g, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]),
    )
    for tpl_name, binarize in pairs:
        template = cv2.imread(str(base_dir / tpl_name), cv2.IMREAD_GRAYSCALE)
        if template is None:
            continue
        screen_bin = binarize(screen)
        for scale in scales:
            scaled = cv2.resize(template, None, fx=scale, fy=scale)
            sw, sh = scaled.shape[1], scaled.shape[0]
            if sh > h_s or sw > w_s:
                continue
            res = cv2.matchTemplate(screen_bin, scaled, cv2.TM_CCOEFF_NORMED)
            _, mv, _, ml = cv2.minMaxLoc(res)
            if mv > best_val:
                best_val, best_loc, best_sw, best_sh = mv, ml, sw, sh
    # 阈值0.4：二值化形状匹配值高（实测0.65+），误匹配低
    if best_val >= 0.4 and best_loc is not None:
        sx = best_loc[0] + best_sw // 2
        sy = best_loc[1] + best_sh // 2
        # 位置约束：打叉在弹窗正下方（下半屏 y>0.4h），上半屏匹配视为误报
        if sy < int(h_s * 0.4):
            return None
        return {"x": sx, "y": sy}
    return None


def _click_reload(ex: SafeExecutor) -> bool:
    """检测「出错了/正在检修中」页面并点击「重新加载」（验证码通过后可能出现）"""
    try:
        shot = _shot_path(ex, "reload_check.png")
        ex.driver.screenshot(shot)
        if cv2.imread(shot) is None:
            ex.driver.screenshot(shot)
        btn = None
        for r in OCR.recognize(shot, detail="all"):
            if "重新加载" in r["text"]:
                box = r["bbox"]
                btn = {"x": (box[0][0] + box[2][0]) // 2, "y": (box[0][1] + box[2][1]) // 2}
                break
        if not btn:
            return False
        print(f"    点击「重新加载」: ({btn['x']}, {btn['y']})")
        ex.tap(btn["x"], btn["y"])
        time.sleep(3)
        return True
    except Exception as e:
        print(f"    ⚠ 重新加载处理异常: {e}")
        return False


def _has_popup_mask(shot_path: str) -> bool:
    """
    弹窗遮罩检测：弹窗出现时周围被半透明遮罩变暗（暗区比例大幅上升）。
    实测：有弹窗暗区0.18-0.33，无弹窗<0.1，阈值0.12安全区分。
    """
    img = cv2.imread(shot_path, cv2.IMREAD_GRAYSCALE)
    if img is None:
        return False
    dark_ratio = float((img < 80).mean())
    return dark_ratio > 0.12


def _close_ad_popup(ex: SafeExecutor) -> bool:
    """
    检测并关闭广告弹窗：
    ① 遮罩检测（弹窗周围变暗，本地计算零成本，最快最准）
    ② AI 找关闭按钮（云端OCR → AI判断）
    ③ 模板匹配打叉兜底
    """
    try:
        shot = _shot_path(ex, "ad_check.png")
        ex.driver.screenshot(shot)
        if cv2.imread(shot) is None:
            ex.driver.screenshot(shot)
        # 1. 遮罩检测：无弹窗直接返回（不浪费AI调用）
        if not _has_popup_mask(shot):
            return False
        print("    检测到弹窗（周围遮罩变暗）")

        # 2. AI 找关闭按钮（云端OCR → AI判断坐标）
        raw = OCR.recognize(shot, detail="all", engine="cloud")
        info = detect_popup(raw)
        if info["has_popup"] and info.get("close_xy") and len(info["close_xy"]) == 2:
            print(f"    弹窗检测(AI): {info['reason']}")
            print(f"    关闭广告弹窗(AI坐标): ({info['close_xy'][0]}, {info['close_xy'][1]})")
            ex.tap(int(info["close_xy"][0]), int(info["close_xy"][1]))
            time.sleep(1.5)
            return True

        # 3. 模板匹配打叉兜底（弹窗正下方居中的 ×）
        btn = _find_ad_close(shot)
        if btn:
            print(f"    关闭广告弹窗(模板): ({btn['x']}, {btn['y']})")
            ex.tap(btn["x"], btn["y"])
            time.sleep(1.5)
            return True

        print("    ⚠ 检测到弹窗但未找到关闭按钮（AI无坐标+模板未命中）")
        return True
    except Exception as e:
        print(f"    ⚠ 广告弹窗处理异常: {e}")
        return False


def _adb_swipe_up(ex: SafeExecutor, distance: int):
    """ADB 手指从下往上滑，内容下滑"""
    import subprocess
    w, h = ex.driver.window_size()
    swipe_x = w // 2
    seg = 3
    seg_px = distance // seg
    for i in range(seg):
        s = int(h * 0.8) - i * 80
        e = s - seg_px
        if e < 50:
            e = 50
        subprocess.run(
            ["adb", "-s", ex.device_id, "shell", "input", "swipe",
             str(swipe_x), str(s), str(swipe_x), str(e), "400"],
            capture_output=True, timeout=10
        )
        time.sleep(0.35)
    time.sleep(1.4)


def _adb_swipe_left(ex: SafeExecutor, y_ratio: float = 0.3):
    """从右往左滑：切换商品图片轮播（图片在详情页上半部分）"""
    import subprocess
    w, h = ex.driver.window_size()
    y = int(h * y_ratio)
    subprocess.run(
        ["adb", "-s", ex.device_id, "shell", "input", "swipe",
         str(int(w * 0.85)), str(y), str(int(w * 0.15)), str(y), "300"],
        capture_output=True, timeout=10
    )
    time.sleep(1.2)


def _adb_swipe_up_short(ex: SafeExecutor):
    """上滑半屏：说明书页内容可能需滑动才全部可见"""
    import subprocess
    w, h = ex.driver.window_size()
    subprocess.run(
        ["adb", "-s", ex.device_id, "shell", "input", "swipe",
         str(w // 2), str(int(h * 0.7)), str(w // 2), str(int(h * 0.3)), "300"],
        capture_output=True, timeout=10
    )
    time.sleep(1)


def _find_close_btn(shot_path: str) -> Optional[dict]:
    """
    说明书页右上角找打叉关闭按钮（多尺度模板匹配，适配多分辨率）。
    模板 files/close.png，命中返回 {"x","y"}，失败返回 None。
    """
    import os as _os
    tpl_path = str(Path(__file__).parent / "files" / "close.png")
    screen = cv2.imread(shot_path)
    template = cv2.imread(tpl_path)
    if screen is None or template is None:
        return None
    h_s, w_s = screen.shape[:2]
    # 右上角区域（打叉永远在右上角）
    roi_x1, roi_y1 = w_s * 2 // 3, 0
    roi = screen[roi_y1:h_s // 4, roi_x1:w_s]

    # 多尺度模板匹配（分辨率适配：以720p为基准按屏宽比例缩放）
    base_w = 720.0
    scale_ratio = w_s / base_w
    scales = [round(scale_ratio * s, 2) for s in [0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5]]
    best_val, best_loc, best_sw, best_sh = 0, None, 0, 0
    for scale in scales:
        scaled = cv2.resize(template, None, fx=scale, fy=scale)
        sw, sh = scaled.shape[1], scaled.shape[0]
        if sh > roi.shape[0] or sw > roi.shape[1]:
            continue
        res = cv2.matchTemplate(roi, scaled, cv2.TM_CCOEFF_NORMED)
        _, mv, _, ml = cv2.minMaxLoc(res)
        if mv > best_val:
            best_val, best_loc, best_sw, best_sh = mv, ml, sw, sh
        t_edge = cv2.Canny(scaled, 30, 100)
        r_edge = cv2.Canny(roi, 30, 100)
        if t_edge.shape[0] <= r_edge.shape[0] and t_edge.shape[1] <= r_edge.shape[1]:
            res2 = cv2.matchTemplate(r_edge, t_edge, cv2.TM_CCOEFF_NORMED)
            _, mv2, _, ml2 = cv2.minMaxLoc(res2)
            if mv2 > best_val:
                best_val, best_loc, best_sw, best_sh = mv2, ml2, sw, sh
    if best_val >= 0.26 and best_loc is not None:
        sx = roi_x1 + best_loc[0] + best_sw // 2
        sy = best_loc[1] + best_sh // 2
        return {"x": sx, "y": sy}
    return None


def _collect_instructions(ex: SafeExecutor) -> dict:
    """
    商品详情页采集说明书（采完链接后调用）：
    back关分享弹窗 → 左滑商品图片 → 点「查看详细说明」
    → 说明书页截图OCR → 提取批准文号/有效期（最多上滑3次兜底）
    返回 {"approval_no": "", "validity": ""}，任何失败留空不抛异常
    """
    w, h = ex.driver.window_size()
    pfx = f"{ex.device_id}_inst"
    # in_detail: 是否确认停留在商品详情页——主流程据此决定后续资质采集是否安全执行
    result = {"approval_no": "", "validity": "", "in_detail": False}

    # 1. 确认屏幕状态：分享弹窗→back关闭；在详情页→开始；在列表页→跳过（不再back，避免乱退）
    for _try in range(3):
        shot = _shot_path(ex, "inst_check.png")
        ex.driver.screenshot(shot)
        if cv2.imread(shot) is None:
            ex.driver.screenshot(shot)
        texts = [r["text"] for r in OCR.recognize(shot, detail="all")]
        if any("分享到" in t for t in texts):
            print("    关闭分享弹窗")
            ex.driver.press("back")
            time.sleep(1.2)
            continue
        state = _screen_state(texts)
        if state == "detail":
            result["in_detail"] = True
            break
        if state == "list":
            print("    ⚠ 说明书采集跳过：已在列表页（step4未成功进店），不再back")
            return result
        if _try < 2:
            print(f"    屏幕状态[{state}]，back一次重新确认")
            ex.driver.press("back")
            time.sleep(1.2)
        else:
            print("    ⚠ 说明书采集跳过：多次确认仍不在商品详情页")
            return result

    # 2. 左滑切换商品图片，找「查看详细说明」按钮
    _adb_swipe_left(ex, 0.3)
    shot2 = _shot_path(ex, "inst_btn.png")
    ex.driver.screenshot(shot2)
    btn = _find_text_in_area(shot2, "查看详细说明", h)
    if not btn:
        print("    ⚠ 未找到「查看详细说明」，跳过说明书采集")
        return result
    print(f"    说明书按钮: ({btn['x']}, {btn['y']})")
    ex.tap(btn["x"], btn["y"])
    time.sleep(2.5)
    _ai_check_captcha(ex)  # 说明书页可能出现验证码（无关键词检查环节）

    # 3. 说明书页截图 + OCR 提取（模仿美团：批准文号和有效期都找到才停，最多滑3次）
    for attempt in range(4):
        shot3 = _shot_path(ex, "inst_page.png")
        ex.driver.screenshot(shot3)
        inst = parse_instructions(OCR.recognize(shot3, detail="all", engine="cloud"))
        if inst["approval_no"] and inst["validity"]:
            print(f"    说明书: 批准文号={inst['approval_no']} 有效期={inst['validity']}")
            result = inst
            break
        if attempt < 3:
            missing = [k for k in ("approval_no", "validity") if not inst[k]]
            print(f"    说明书字段不全(第{attempt+1}次，缺{missing})，上滑重试")
            _adb_swipe_up_short(ex)
    else:
        print("    ⚠ 说明书页4次均未解析到批准文号/有效期")
        result = inst

    # 4. 说明书采集完成：点右上角打叉关闭说明书页（不能back——back会直接回列表页）
    #    本任务内缓存：第一个商品找到后，后续商品直接复用坐标
    global _CLOSE_BTN_CACHE
    close_btn = _CLOSE_BTN_CACHE
    if close_btn is None:
        close_shot = _shot_path(ex, "inst_close.png")
        ex.driver.screenshot(close_shot)
        close_btn = _find_close_btn(close_shot)
        if close_btn:
            _CLOSE_BTN_CACHE = close_btn
            print(f"    关闭说明书页: ({close_btn['x']}, {close_btn['y']})（本任务已缓存，后续商品复用）")
        else:
            print("    ⚠ 未找到打叉按钮（后续步骤会按屏幕状态自行处理）")
    else:
        print(f"    关闭说明书页: ({close_btn['x']}, {close_btn['y']})（复用本任务缓存坐标）")
    if close_btn:
        ex.tap(close_btn["x"], close_btn["y"])
        time.sleep(1.2)
    return result


def _collect_snapshot(ex: SafeExecutor, title: str) -> str:
    """
    网页快照（采集说明书之后调用）：
    先识别屏幕状态——已在详情页直接拍；说明书页等未知页则back一次回详情页再拍；
    在列表页/店铺页等明确非详情页位置直接跳过（不再back，避免把列表页退到首页）
    """
    try:
        # 1. 先截图识别状态，决定是否需要 back
        shot = _shot_path(ex, "snap_check.png")
        for _try in range(2):
            ex.driver.screenshot(shot)
            if cv2.imread(shot) is None:
                ex.driver.screenshot(shot)
            texts = [r["text"] for r in OCR.recognize(shot, detail="all")]
            state = _screen_state(texts)
            if state == "detail":
                break
            if state in ("list", "shop"):
                print(f"    ⚠ 快照跳过：屏幕状态[{state}]，不在详情页也不再back")
                return ""
            if _try == 0:
                # 说明书页/其他未知页：back 一次回详情页再确认
                print(f"    快照：屏幕状态[{state}]，back回详情页")
                ex.driver.press("back")
                time.sleep(1.2)
            else:
                print(f"    ⚠ 快照跳过：back后屏幕状态[{state}]，不在详情页")
                return ""
        else:
            print("    ⚠ 快照跳过：无法确认在详情页")
            return ""

        # 2. 滚动截图 + 上传OSS（美团同款逻辑在 snapshot 模块）
        url, snap_reason = collect_snapshot(ex.driver, title, ex.device_id)
        print(f"    📷 快照: {url if url else f'失败({snap_reason})'}")
        if url:
            # 快照滚动改变了页面位置：back 回店铺页，再开始资质采集
            ex.driver.press("back")
            time.sleep(1.2)
        return url
    except Exception as e:
        print(f"    ⚠ 快照采集异常: {e}")
        return ""


def _collect_license(ex: SafeExecutor, shop_name: str) -> dict:
    """
    采集商家资质（采完说明书后调用，屏幕在说明书页）：
    back回店铺内 → OCR上半区找店铺名点击 → 下半区找「查看营业资质」点击
    → 云端OCR找「资质编号」取值 → 点编号下方约3cm打开营业执照 → 百度营业执照专用接口OCR
    返回 {"license_no": "", "license": {}}，任何失败留空不抛异常
    """
    w, h = ex.driver.window_size()
    pfx = f"{ex.device_id}_lic"
    result = {"license_no": "", "license": {}}

    # 0. 已采集过的店铺：直接从数据库获取资质，不重复采集（美团/PDD同款）
    try:
        exist = get_existing_license(shop_name)
        if exist.get("license_no") or exist.get("company"):
            print(f"    资质已存在，从数据库获取: 编号={exist['license_no'][:24]} 公司={exist['company'][:20]}")
            result["license_no"] = exist["license_no"]
            lic = {}
            if exist.get("company"):
                lic["单位名称"] = exist["company"]
            if exist.get("address"):
                lic["地址"] = exist["address"]
            result["license"] = lic
            return result
    except Exception as e:
        print(f"    ⚠ 查询已有资质失败: {e}")

    # 1+2. 回到店铺页并采资质（最多2轮）：每轮先找店铺名，找不到/点后无资质入口就 back 回退一层再看
    #      注意不能无条件back：屏幕已在店铺页时再退会到列表页
    def _find_license_btn():
        # 先搜下半区，找不到整张图分析（「查看营业资质」位置不固定，有时在上半区）
        for rect in ([0, int(h * 0.45), w, h], None):
            for r in OCR.recognize(shot2, rect=rect, detail="all"):
                if "查看营业资质" in r["text"]:
                    box = r["bbox"]
                    return {"x": (box[0][0] + box[2][0]) // 2, "y": (box[0][1] + box[2][1]) // 2}
        return None

    lic_btn = None
    for attempt in range(2):
        if _is_list_page(ex):
            print("    ⚠ 资质采集跳过：已在列表页（不点列表卡片）")
            return result
        shot = _shot_path(ex, "lic_shopname.png")
        ex.driver.screenshot(shot)
        shop_btn = _find_text_in_area(shot, shop_name, h // 2)
        if shop_btn:
            print(f"    店铺名: ({shop_btn['x']}, {shop_btn['y']}) (第{attempt+1}轮)")
            ex.tap(shop_btn["x"], shop_btn["y"])
            time.sleep(2.5)

            # 下半区找「查看营业资质」，找不到先上滑一次再看
            shot2 = _shot_path(ex, "inst_btn.png")
            ex.driver.screenshot(shot2)
            lic_btn = _find_license_btn()
            if not lic_btn:
                print(f"    未找到「查看营业资质」(第{attempt+1}轮)，上滑再看")
                _adb_swipe_up_short(ex)
                shot2 = _shot_path(ex, "inst_btn.png")
                ex.driver.screenshot(shot2)
                lic_btn = _find_license_btn()
            if lic_btn:
                break
        if attempt == 0:
            # 屏幕可能在详情页/说明书页：back 回退一层再看
            print("    back一次回退后再试")
            ex.driver.press("back")
            time.sleep(1.5)
        else:
            print("    ⚠ 资质采集跳过：未找到店铺名或「查看营业资质」")
            return result
    print(f"    查看营业资质: ({lic_btn['x']}, {lic_btn['y']})")
    ex.tap(lic_btn["x"], lic_btn["y"])
    time.sleep(2.5)
    _ai_check_captcha(ex)  # 资质页可能出现验证码（无关键词检查环节）

    # 4. 资质页：云端OCR找「资质编号」取值
    shot3 = _shot_path(ex, "lic_no.png")
    ex.driver.screenshot(shot3)
    raw3 = OCR.recognize(shot3, detail="all", engine="cloud")
    license_no = extract_value_after(raw3, "资质编号")
    if license_no:
        # OCR可能把长编号读散（如 "91450800MA5 KEHAHXT"），编号不该有空格，清洗掉
        license_no = license_no.replace(" ", "")
    if not license_no:
        print("    ⚠ 资质采集跳过：未找到资质编号")
        return result
    print(f"    资质编号: {license_no}")
    result["license_no"] = license_no

    # 5. 点资质编号下方约3cm（≈0.2屏高）打开营业执照大图
    #    注意：云端OCR(标准版)无坐标，必须用本地OCR找资质编号的真实位置
    no_x, no_y = None, None
    for r in OCR.recognize(shot3, detail="all"):  # 本地引擎，有真实bbox
        t = r["text"].strip().rstrip(":： \t")
        if t.startswith("资质编号"):
            box = r["bbox"]
            no_x = (box[0][0] + box[2][0]) // 2
            no_y = (box[0][1] + box[2][1]) // 2
            break
    if no_y is None:
        print("    ⚠ 资质采集跳过：本地OCR未找到资质编号位置")
        return result
    print(f"    资质编号位置: ({no_x}, {no_y})，点击下方打开执照")
    ex.tap(no_x, min(no_y + int(h * 0.2), h - 50))
    time.sleep(2.5)

    # 6. 营业执照截图 + 百度营业执照专用接口（美团同款；执照在屏幕中下部，先裁剪再识别，逐级兜底）
    shot4 = _shot_path(ex, "lic_license.png")
    ex.driver.screenshot(shot4)
    lic = {}
    for t_ratio, b_ratio in [(0.35, 0.8), (0.4, 1.0), (0.0, 1.0)]:
        lic = OCR.recognize_license(shot4, rect=[0, int(h * t_ratio), w, int(h * b_ratio)])
        if lic:
            break
    if lic:
        print(f"    营业执照: {lic}")
        result["license"] = lic
    else:
        print("    ⚠ 营业执照OCR为空")
    return result


def _detect_right_col_split(raw: list, w: int) -> Optional[int]:
    """
    定位列表页"图片|文字"分界x：每张卡片都有价格，￥全在右列且x一致。
    取￥块左边缘x的中位数 − 15 作为分界（图片区在左、标题/价格在右）。
    实测3台设备：分界499时图片文字最右仅474，过滤干净。返回None=检测不到（用整图）。
    """
    price_x = sorted(r["box"][0] for r in raw if "￥" in r["text"] or "¥" in r["text"])
    if len(price_x) < 3:
        return None
    return max(price_x[len(price_x) // 2] - 15, int(w * 0.3))


def _get_named_shops(ex: SafeExecutor, shot_name: str, keyword: str = "") -> tuple:
    """截图 + OCR + AI → 返回 (有店铺名的列表, 本地OCR原始结果带坐标)"""
    shot = _screenshot(ex, shot_name)
    raw = OCR.recognize(shot, detail="all")
    w, h = ex.driver.window_size()

    # 只保留右列（商品描述列）：用￥定位分界，过滤左侧图片文字（包装字/英文/乱码）
    split_x = _detect_right_col_split(raw, w)
    if split_x:
        filtered = [r for r in raw if (r["box"][0] + r["box"][2]) // 2 >= split_x]
        if filtered:
            print(f"[step3] 右列识别: 分界x={split_x}，过滤掉{len(raw) - len(filtered)}块图片文字")
            raw = filtered
            # 在截图副本上画红线保存（出错时核对分界是否偏左/偏右）
            try:
                img = cv2.imread(shot)
                if img is not None:
                    cv2.line(img, (split_x, 0), (split_x, img.shape[0]), (0, 0, 255), 3)
                    cv2.putText(img, f"split={split_x}", (split_x + 5, 50),
                                cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
                    cv2.imwrite(shot.replace(".png", "_split.png"), img)
            except Exception:
                pass

    if USE_VISION_PARSER:
        # 方案A：截图送视觉模型（生僻字更准），裁掉左列商品图片只留右列文字区
        # crop_x=split_x 来自OCR分界检测，AI只看文字区，y取标题和店铺名的中点
        shops = VisionParser().parse_shops(shot, screen_size=(w, h), keyword=keyword, crop_x=split_x or 0)
        # AI估y不准会点到上一个店铺，用OCR精确坐标修正
        _correct_vision_coords_with_ocr(shops, raw)
        # 视觉模型失败时 fallback 到 OCR+文本AI
        if not shops:
            print("[step3] 视觉模型未识别到店铺，fallback 到 OCR+文本AI")
            shops = AIParser().parse_shops(raw, screen_size=(w, h), keyword=keyword)
    else:
        # 原方案：本地OCR文本 → 文本AI解析
        shops = AIParser().parse_shops(raw, screen_size=(w, h), keyword=keyword)
    # 只保留有效店铺名+价格：店铺名必须含中文或字母（排除纯数字/标点/空格）
    import re as _re
    valid = []
    for s in shops:
        name = (s[0] or "").strip()
        price = (s[2] or "").strip()
        if name and _re.search(r'[一-鿿＀-￯a-zA-Z]', name) and price:
            valid.append(s)
    return valid, raw


def _find_title_y(raw: list, title: str) -> Optional[int]:
    """在本地OCR结果里找与标题开头重合最多的块的y坐标（标题行位置，用于裁剪）"""
    nt = normalize_match_text(title)
    best_len, best_cy = 0, None
    for r in raw:
        t = normalize_match_text(r["text"])
        if not t:
            continue
        n = 0
        for a, b in zip(t, nt):
            if a == b:
                n += 1
            else:
                break
        if n > best_len:
            best_len = n
            best_cy = (r["bbox"][0][1] + r["bbox"][2][1]) // 2
    return best_cy if best_len >= 2 else None


def _correct_vision_coords_with_ocr(shops: list, raw: list) -> None:
    """用OCR精确坐标修正VisionParser返回的y坐标。
    AI估y不准会点到上一个店铺，用OCR里店铺名和标题的精确y重新算中点。
    直接修改 shops 列表中的坐标，不返回新列表。"""
    for s in shops:
        shop_name = str(s[0] or "").strip()
        title = str(s[1] or "").strip()
        # 在OCR里找店铺名 → 精确y（子串匹配，取最长匹配的块）
        shop_y = None
        if shop_name and len(shop_name) >= 2:
            ns = normalize_match_text(shop_name)
            best_len, best_y = 0, None
            for r in raw:
                t = normalize_match_text(r["text"])
                if not t or len(t) < 2:
                    continue
                if ns in t or t in ns:
                    if len(t) > best_len:
                        best_len = len(t)
                        best_y = (r["bbox"][0][1] + r["bbox"][2][1]) // 2
            shop_y = best_y
        # 在OCR里找标题 → 精确y（复用已有函数）
        title_y = _find_title_y(raw, title) if title else None
        # 用OCR坐标修正点击坐标
        if shop_y is not None and title_y is not None:
            s[3][1] = (title_y + shop_y) // 2  # 点击y = 标题和店铺名中点
            s[4] = shop_y
            print(f"    [coord] OCR修正: {shop_name[:10]} y={s[3][1]} (标题y={title_y} 店铺y={shop_y})")
        elif shop_y is not None:
            s[3][1] = max(shop_y - 150, 0)  # 只有店铺名y，往上150点击
            s[4] = shop_y
            print(f"    [coord] OCR修正(仅店铺名): {shop_name[:10]} y={s[3][1]}")


def _shop_key(shop: list) -> str:
    """用店铺名+价格去重（去括号内分店名、去尾部点号）"""
    import re
    name = shop[0]
    price = shop[2] if len(shop) > 2 else ""
    name = name.replace("(", "（").replace(")", "）")
    name = re.sub(r'（.*', '', name)
    name = re.sub(r'[.．…]+$', '', name)
    return f"{name.strip()}|{price.strip()}"


def _visit_shop(ex: SafeExecutor, shop: list, visited: set, keyword: str = "", task: dict = None) -> dict:
    """
    点击进入店铺 → step4 → 说明书 → 快照 → 资质 → 返回完整数据 dict
    task: 调度任务 dict（task_id/enterprise_id/collect_round等），手动模式传 None
    """
    """点击进入店铺 → step4 → 返回完整数据 dict"""
    key = _shop_key(shop)
    if key in visited:
        return None

    visited.add(key)
    shop_name = shop[0]
    product_title = shop[1]
    price = shop[2]
    sales = str(shop[5]) if len(shop) > 5 and shop[5] else ""
    click_x, click_y = shop[3]
    print(f"  → 进入 [{shop_name}] 商品: {product_title[:30]}  价格: {price}  月售: {sales}")

    ex.tap(click_x, click_y)

    try:
        qr_url = step4_parse_qr(ex, product_title, shop_name, shop_xy=[click_x, click_y])
    except Exception as e:
        print(f"    ⚠ step4异常: {e}，跳过此店铺")
        qr_url = ""

    if qr_url == "__TERMINATE__":
        print(f"    ⚠ 遇到终止信号，停止遍历")
        return {"__terminate__": True}

    if qr_url == "__RESTART__":
        print(f"    ⚠ 页面异常，触发重启恢复")
        return {"__restart__": True}

    if qr_url:
        print(f"    ✅ QR: {qr_url[:80]}")
        print(f"    📦 采集完成: {shop_name} | {product_title[:30]} | {price} | 月售{sales} | {qr_url[:60]}")
    else:
        print(f"    ⚠ 未获取到二维码链接")
        print(f"    📦 采集完成(无链接): {shop_name} | {product_title[:30]} | {price} | 月售{sales}")

    # 内存熔断：本地OCR已OOM → 说明书/快照/资质全部跳过（状态检测不可靠，避免乱back乱点）
    if getattr(OCR, "oom", False):
        print("    ⚠ 内存不足熔断：跳过说明书/快照/资质，直接返回列表页（请关闭部分程序释放内存）")
        inst = {"approval_no": "", "validity": "", "in_detail": False}
        snapshot_url = ""
        lic = {"license_no": "", "license": {}}
    else:
        # 采完链接后采集说明书（批准文号/有效期），失败不阻塞
        try:
            inst = _collect_instructions(ex)
        except Exception as e:
            print(f"    ⚠ 说明书采集异常: {e}（疑似内存不足，请关闭部分程序）")
            inst = {"approval_no": "", "validity": "", "in_detail": False}
        print(f"    📄 说明书: 批准文号={inst['approval_no']} 有效期={inst['validity']}")

        # 采完说明书后采集网页快照（美团同款顺序：说明书→快照→资质），失败不阻塞
        snapshot_url = _collect_snapshot(ex, product_title)

        # 采完快照后采集商家资质（_collect_license 会先 back 回店铺页，再按屏幕状态自校验：列表页/无店铺名都跳过）
        try:
            lic = _collect_license(ex, shop_name)
        except Exception as e:
            print(f"    ⚠ 资质采集异常: {e}")
            lic = {"license_no": "", "license": {}}
    print(f"    📋 资质编号: {lic['license_no']}  单位名称: {lic['license'].get('单位名称', '')}  信用代码: {lic['license'].get('社会信用代码', '')}")

    # 返回搜索页：先检测再退（已是列表页则一步不退；退到首页立即停，防止退过头退出app）
    for _ in range(5):
        try:
            pos = _where_am_i(ex)
        except Exception as e:
            print(f"    ⚠ 位置检测异常({e})，停止返回")
            break
        if pos == "list":
            break
        if pos == "home":
            print("    ⚠ 已退到首页（可能退过头），停止返回")
            break
        ex.driver.press("back")
        time.sleep(1.4)

    task = task or {}
    return {
        "shop": shop_name,
        "title": product_title,
        "price": price,
        "sales": sales,
        "approval_no": inst["approval_no"],
        "validity": inst["validity"],
        "license_no": lic["license_no"],
        "license": lic["license"],
        "snapshot_url": snapshot_url,
        "search_name": keyword,
        "link": qr_url or "",
        "task_id": task.get("task_id"),
        "enterprise_id": task.get("enterprise_id"),
        "collect_round": task.get("collect_round"),
        "collect_equipment_account_id": task.get("collect_equipment_account_id"),
        "collect_region_id": task.get("collect_region_id"),
        "collect_config_info": task.get("collect_config_info", ""),
    }


def _captcha_log_path(device_id: str) -> Path:
    """每台设备独立的验证码日志文件（独立计数，互不影响）"""
    return CAPTCHA_LOG_DIR / f"captcha_log_{device_id}.txt"


def _log_captcha(ex: SafeExecutor) -> bool:
    """
    记录验证码出现时间到日志，检查一天≥8次停止。
    返回 True=已触发停止（上层不再休息），False=正常（处理完成后按频率休息）。
    """
    global CAPTCHA_ABORTED, CAPTCHA_ABORT_REASON
    import os as _os
    ts = time.strftime("%Y-%m-%d %H:%M:%S")
    line = f"{ts} 验证码出现"
    try:
        _os.makedirs(_os.path.dirname(_captcha_log_path(ex.device_id)), exist_ok=True)
        with open(_captcha_log_path(ex.device_id), "a", encoding="utf-8") as f:
            f.write(line + "\n")
    except Exception as e:
        print(f"    [验证码记录] 写日志失败: {e}")
    print(f"    [验证码记录] {line}")

    # 一天内 ≥8次 → 立即停止采集回告（风控可能封号）
    today_count = _captcha_count_today(ex.device_id)
    print(f"    [验证码记录] 设备{ex.device_id}今天已出现{today_count}次")
    if today_count >= CAPTCHA_DAILY_LIMIT:
        CAPTCHA_ABORTED = True
        CAPTCHA_ABORT_REASON = f"一天内验证码达{today_count}次，进入风控可能封号"
        print(f"    ⚠ {CAPTCHA_ABORT_REASON}，停止采集")
        return True
    return False


def _captcha_count_today(device_id: str) -> int:
    """统计该设备日志中今天（按日期）的验证码次数"""
    try:
        today = time.strftime("%Y-%m-%d")
        count = 0
        with open(_captcha_log_path(device_id), encoding="utf-8") as f:
            for line in f:
                if line.startswith(today):
                    count += 1
        return count
    except Exception:
        return 0


def _save_captcha_check(ex: SafeExecutor) -> None:
    """每次判定出现验证码时截图保存到 logs/captcha_check/{日期}/，人工确认是否真验证码"""
    import os as _os
    try:
        day = time.strftime("%Y-%m-%d")
        d = CAPTCHA_CHECK_DIR / day
        d.mkdir(parents=True, exist_ok=True)
        ts = time.strftime("%H%M%S")
        path = str(d / f"{ts}_{ex.device_id}_captcha.png")
        ex.driver.screenshot(path)
        print(f"    [验证码截图] 已保存: {path}")
    except Exception as e:
        print(f"    [验证码截图] 保存失败: {e}")


def _handle_captcha(ex: SafeExecutor, ocr_texts: list) -> bool:
    """处理验证码, 重试5次, 失败等人工, 返回True=已解决；
    只有自动解决成功才计数（失败走人工的不算），成功后按频率休息"""
    _save_captcha_check(ex)  # 判定出现验证码时截图存档（人工确认是否误判）
    import sys as _sys
    _sys.path.insert(0, r"D:\drug\sg\yzm")

    solved = False
    for attempt in range(1, 6):
        print(f"    [验证码] 第{attempt}次尝试...")
        nine_kw = any("提交" in t or "没有新图片" in t for t in ocr_texts)
        if nine_kw:
            from nine_grid import solve as solve_nine
            ok = solve_nine(ex.driver)
        else:
            from tmp_captcha_test6 import solve_slider
            ok = solve_slider(ex.driver)
        if ok:
            print(f"    ✅ 验证码已解决")
            solved = True
            break
        print(f"    ❌ 第{attempt}次失败")
        time.sleep(1)

    if solved:
        # 只有解决成功才计数 + 一天≥8次停止检查 + 每2次休息30分钟
        aborted = _log_captcha(ex)
        if not aborted:
            _captcha_rest(ex)
    else:
        print(f"    ⚠ 5次自动处理失败, 请人工处理...（不计数）")
        input("    处理完成后按回车继续...")
    return True


def _captcha_rest(ex: SafeExecutor) -> None:
    """验证码处理完成后：每累计2次休息30分钟；休息期间每10分钟回告一次进度（防后台判假死）"""
    today_count = _captcha_count_today(ex.device_id)
    if today_count % CAPTCHA_REST_EVERY == 0 and today_count < CAPTCHA_DAILY_LIMIT:
        print(f"    ⏸ 已出现{today_count}次验证码，休息{CAPTCHA_REST_MINUTES}分钟...")
        rest_left = CAPTCHA_REST_MINUTES * 60
        while rest_left > 0:
            chunk = min(rest_left, 600)  # 每10分钟一段
            time.sleep(chunk)
            rest_left -= chunk
            # 休息期间每10分钟回告当前进度（后台超过10分钟无回告会判假死）
            if rest_left > 0 and _SCHEDULER is not None:
                try:
                    _SCHEDULER.post_report({
                        "task_id": _TASK_ID,
                        "platform": _SCHEDULER.platform,
                        "username": _SCHEDULER.username,
                        "is_finished": 0,
                        "need_reassign": 0,
                        "current_page": CURRENT_PAGE,
                        "crawled_count": _CRAWLED_COUNT,
                    })
                    print(f"    [休息中回告] 当前页{CURRENT_PAGE}，已采{_CRAWLED_COUNT}条")
                except Exception as e:
                    print(f"    [休息中回告] 失败: {e}")
        print(f"    ▶ 休息结束，继续采集")


# 验证码强特征词（店铺页/列表页文字多但无这些词——AI判risk时用OCR文字二次确认防误判）
CAPTCHA_KW = ("拖动滑块", "请按住滑块", "请按照说明", "点我反馈", "进行验证",
              "滑块验证", "拼图", "安全验证", "图形验证", "点击完成验证", "没有新图片", "操作频繁")


def _has_captcha_kw(texts: list) -> bool:
    """OCR文字是否含验证码强特征词"""
    joined = "".join(texts)
    return any(k in joined for k in CAPTCHA_KW)


def _ai_check_captcha(ex: SafeExecutor) -> bool:
    """
    AI检测当前屏幕是否出现验证码（用于没有关键词检查的环节）：
    截图 → 本地OCR → 强特征词预筛 → AI判断页面类型（risk=验证码）→ 有则自动处理。
    返回 True=检测到验证码（已处理或处理中），False=无验证码。
    """
    try:
        shot = _shot_path(ex, "captcha_ai.png")
        ex.driver.screenshot(shot)
        if cv2.imread(shot) is None:
            ex.driver.screenshot(shot)
        raw = OCR.recognize(shot, detail="all")  # 本地OCR即可（验证码文字是大字，不用百度）
        # 预筛：OCR文本里连强特征词都没有 → 不是验证码，不调AI（省调用+防误判）
        if not _has_captcha_kw([r["text"] for r in raw]):
            return False
        page = AIParser().check_page(raw)
        if page.get("type") == "risk":
            print(f"    ⚠ AI检测到验证码页面: {page.get('detail', '')}")
            _handle_captcha(ex, [r["text"] for r in raw])
            time.sleep(2)
            return True
        return False
    except Exception as e:
        print(f"    ⚠ AI验证码检测异常: {e}")
        return False


def _check_account_kicked(ex: SafeExecutor) -> bool:
    """
    检测账号是否被踢/封号：登录页元素（me.ele:id/login_onkey_login_ll）出现即判定。
    检测到 → 置 ACCOUNT_ABORTED 标志（停止采集 + 调度回告）。
    """
    global ACCOUNT_ABORTED
    try:
        if ex.driver.xpath('//*[@resource-id="me.ele:id/login_onkey_login_ll"]').exists:
            print("    ⚠ 检测到账号被踢/封号（登录页），停止采集")
            ACCOUNT_ABORTED = True
            return True
    except Exception as e:
        print(f"    ⚠ 封号检测异常: {e}")
    return False


def step4_parse_qr(ex: SafeExecutor, product_title: str, shop_name: str = "",
                   shop_xy: Optional[list] = None) -> str:
    """
    1. 等待加载 → OCR → AI找商品标题坐标
    2. 点击商品标题 → 进入商品详情
    3. 找右上角"分享" → 点击 → 二维码弹窗
    4. 截图 → pyzbar 解析二维码
    返回 URL 或空字符串
    """
    # 安全的文件名前缀（用hash避免中文路径cv2兼容问题）
    # 多设备隔离：加入设备ID，防止并发时两台设备写同一个文件
    import hashlib
    _hash = hashlib.md5(shop_name.encode()).hexdigest()[:8] if shop_name else "unknown"
    _pfx = lambda name: _shot_path(ex, f"_s4_{_hash}_{name}")

    time.sleep(6)

    # ── 检测页面类型：验证码/风控/正常 ──
    # unknown恢复流程：等1秒二次截图确认 → 3次确认unknown → back一层+重新点击进入（刷新）→ 再检测 → 仍unknown才重启
    unknown_round = 0
    backed_once = False
    for page_retry in range(9):
        shot_check = _pfx("page_check.png")
        ex.driver.screenshot(shot_check)
        check_raw = OCR.recognize(shot_check, detail="all")

        # 方法A: 模板匹配检测验证码
        import os as _os
        captcha_tpl = str(Path(__file__).parent / "files" / "captcha1.png")
        if _os.path.exists(captcha_tpl):
            si = cv2.imread(shot_check)
            ti = cv2.imread(captcha_tpl)
            if si is not None and ti is not None:
                gs = cv2.cvtColor(si, cv2.COLOR_BGR2GRAY)
                gt = cv2.cvtColor(ti, cv2.COLOR_BGR2GRAY)
                h_s, w_s = gs.shape
                crop_y1, crop_y2 = int(h_s * 0.25), int(h_s * 0.75)
                crop_x1, crop_x2 = 0, 400
                gs_crop = gs[crop_y1:crop_y2, crop_x1:crop_x2]
                scores = []
                for fn, ss, tt in [
                    ("gray", gs_crop, gt),
                    ("edge", cv2.Canny(gs_crop,30,100), cv2.Canny(gt,30,100)),
                    ("hist", cv2.equalizeHist(gs_crop), cv2.equalizeHist(gt)),
                    ("blur", cv2.GaussianBlur(gs_crop,(3,3),0), cv2.GaussianBlur(gt,(3,3),0)),
                    ("otsu", cv2.threshold(gs_crop,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1],
                            cv2.threshold(gt,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]),
                ]:
                    if ss.ndim == 2 and tt.ndim == 2 and ss.shape[0] >= tt.shape[0] and ss.shape[1] >= tt.shape[1]:
                        r = cv2.matchTemplate(ss, tt, cv2.TM_CCOEFF_NORMED)
                        _, mv, _, _ = cv2.minMaxLoc(r)
                        scores.append((mv, fn))
                if scores:
                    best_v = max(s[0] for s in scores)
                    best_m = max(scores, key=lambda s: s[0])[1]
                    print(f"    验证码模板匹配: {best_m}={best_v:.3f}")
                    captcha_kw = any("拖动滑块" in r["text"] or "请按住滑块" in r["text"] or "安全验证" in r["text"] for r in check_raw)
                    nine_kw = any("提交" in r["text"] or "没有新图片" in r["text"] for r in check_raw)
                    if best_v >= 0.30 and (captcha_kw or nine_kw):
                        print(f"    ⚠ 检测到验证码，尝试自动处理...")
                        if _handle_captcha(ex, [r["text"] for r in check_raw]):
                            continue
                        return "__TERMINATE__"
                    elif best_v >= 0.30 and not captcha_kw:
                        print(f"    ⚠ 模板匹配命中但OCR无验证码关键词，忽略")
        page_type = AIParser().check_page(check_raw)
        ptype = page_type.get("type", "unknown")

        if ptype == "risk":
            # OCR文字二次确认：店铺页/列表页文字多但无验证码特征词 → 误判，忽略
            if not _has_captcha_kw([r["text"] for r in check_raw]):
                print("    AI判risk但OCR无验证码特征词，忽略（店铺页/列表页误判）")
                continue
            print(f"    ⚠ AI检测到验证码，尝试自动处理...")
            if _handle_captcha(ex, [r["text"] for r in check_raw]):
                continue
            return "__TERMINATE__"

        if ptype == "home":
            # 二次确认：截图太早页面没加载完时AI可能误判首页（底部导航"我的"在任何页面都可见）
            time.sleep(1)
            ex.driver.screenshot(shot_check)
            confirm_raw = OCR.recognize(shot_check, detail="all")
            ptype2 = AIParser().check_page(confirm_raw).get("type", "unknown")
            if ptype2 == "home":
                print("    ⚠ 二次确认仍为首页（被踢回/退过头），触发重启恢复")
                return "__RESTART__"
            if ptype2 == "normal":
                break  # 页面加载完成，恢复正常
            print(f"    AI先判home，二次确认为{ptype2}，继续检测")
            continue

        if ptype == "list":
            print("    ⚠ AI检测到列表页（未成功进店），放弃本店")
            return ""

        if ptype == "login":
            global ACCOUNT_ABORTED
            ACCOUNT_ABORTED = True
            print("    ⚠ AI检测到登录页（账号被踢/封号），停止采集")
            return "__TERMINATE__"

        if ptype == "normal":
            break  # 正常，跳出重试循环

        # qrcode/unknown：等1秒二次截图确认（点击后立即截图可能页面没加载完，避免误判）
        time.sleep(1)
        ex.driver.screenshot(shot_check)
        confirm_raw = OCR.recognize(shot_check, detail="all")
        ptype2 = AIParser().check_page(confirm_raw).get("type", "unknown")
        if ptype2 == "normal":
            break
        if ptype2 == "risk":
            if not _has_captcha_kw([r["text"] for r in confirm_raw]):
                print("    二次确认risk但OCR无验证码特征词，忽略")
                continue
            if _handle_captcha(ex, [r["text"] for r in confirm_raw]):
                continue
            return "__TERMINATE__"
        if ptype2 == "home":
            print("    ⚠ 二次确认检测到首页，触发重启恢复")
            return "__RESTART__"
        if ptype2 == "list":
            print("    ⚠ 二次确认检测到列表页，放弃本店")
            return ""
        if ptype2 == "login":
            ACCOUNT_ABORTED = True  # 本函数已声明global
            print("    ⚠ 二次确认检测到登录页（账号被踢/封号），停止采集")
            return "__TERMINATE__"

        # 两次都是unknown → 确认计数
        unknown_round += 1
        print(f"    确认unknown(第{unknown_round}次)")
        if unknown_round >= 3:
            if not backed_once:
                # 3次确认unknown：back一层 + 重新点击进入一次（类似刷新）
                print("    3次确认unknown，back一层并重新点击进入")
                ex.driver.press("back")
                time.sleep(1.5)
                if shop_xy:
                    ex.tap(shop_xy[0], shop_xy[1])
                    time.sleep(2.5)
                backed_once = True
                unknown_round = 0
            else:
                # 刷新后仍unknown → 重启恢复
                import shutil
                err_dir = SCREENSHOT_DIR / ex.device_id / "step4" / "unrecognized"
                err_dir.mkdir(exist_ok=True)
                shutil.copy(shot_check, str(err_dir / f"unknown_{int(time.time())}.png"))
                print("    ⚠ 刷新后仍unknown，触发重启恢复")
                return "__RESTART__"
        time.sleep(2)
    else:
        # 检测循环耗尽仍未正常 → 重启恢复
        import shutil
        err_dir = SCREENSHOT_DIR / ex.device_id / "step4" / "unrecognized"
        err_dir.mkdir(exist_ok=True)
        shutil.copy(shot_check, str(err_dir / f"unknown_{int(time.time())}.png"))
        print(f"    ⚠ 检测循环耗尽({ptype})，触发重启恢复")
        return "__RESTART__"
    # normal → 继续

    # ── 店铺页判断：OCR同时检测到「刚刚搜过」和「评价」说明在店铺页 ──
    in_shop = False
    for _ in range(10):
        shop_check = _pfx("shop_check.png")
        ex.driver.screenshot(shop_check)
        shop_raw = OCR.recognize(shop_check, detail="text")
        has_ganggang = any("刚刚搜过" in t for t in shop_raw)
        has_pingjia = any("评价" in t for t in shop_raw)
        if has_ganggang and has_pingjia:
            in_shop = True
            print(f"    已确认在店铺页")
            break
        time.sleep(1)
    if not in_shop:
        print(f"    ⚠ 未检测到店铺页，继续尝试...")

    # ── 第1步：截图 + AI找商品标题坐标（AI失败或返回None时重试3次，页面可能未加载完）──
    system_prompt = """你收到店铺页的OCR文字。商品标题文字坐标已知(从OCR中有x,y)。
请找到和以下商品标题匹配的文字块，返回其点击坐标。

【重要规则】
- 坐标必须从OCR数据中选取，不得编造或估算
- 如果找不到完全匹配的，找最相似的
- 如果完全找不到，返回null

只返回JSON：
{"title_xy": [x, y] 或 null, "shop": "店铺名"}"""

    parser = AIParser()
    title_xy = None
    for ai_try in range(3):
        shot = _pfx("shop.png")
        ex.driver.screenshot(shot)
        raw = OCR.recognize(shot, detail="all")

        sorted_r = sorted(raw, key=lambda r: r["bbox"][0][1])
        lines = []
        for r in sorted_r:
            cx = (r["bbox"][0][0] + r["bbox"][2][0]) // 2
            cy = (r["bbox"][0][1] + r["bbox"][2][1]) // 2
            lines.append(f"[x={cx:4d}, y={cy:4d}] {r['text']}")
        ocr_text = "\n".join(lines)

        resp = parser._call(system_prompt, f"商品标题: {product_title}\n\nOCR文字：\n{ocr_text}\n\n请返回商品标题坐标。")

        import json
        cleaned = resp.strip()
        if cleaned.startswith("```"):
            cl = cleaned.split("\n")
            if cl[0].startswith("```"): cl = cl[1:]
            if cl and cl[-1].strip() == "```": cl = cl[:-1]
            cleaned = "\n".join(cl).strip()
        try:
            data = json.loads(cleaned)
            title_xy = data.get("title_xy")
        except json.JSONDecodeError:
            title_xy = None

        if title_xy and isinstance(title_xy, list) and len(title_xy) == 2:
            break
        print(f"    ⚠ AI未返回有效坐标(第{ai_try+1}次): {title_xy}，2秒后重试")
        time.sleep(2)

    if not title_xy or not isinstance(title_xy, list) or len(title_xy) != 2:
        print(f"    ⚠ AI 3次均未返回有效坐标: {title_xy}")
        return ""

    tx, ty = title_xy
    if tx is None or ty is None:
        print(f"    ⚠ AI返回空坐标")
        return ""

    tx, ty = int(tx), int(ty)
    w, h = ex.driver.window_size()
    if not (0 <= tx <= w and 0 <= ty <= h):
        print(f"    ⚠ 坐标越界: ({tx},{ty}) 超出屏幕 {w}x{h}")
        return ""

    # ── 第2步：点击商品标题 → 进入商品详情（最多重试3次）──
    entered_detail = False
    for attempt in range(3):
        print(f"    点击商品标题: ({tx},{ty}) (第{attempt+1}次)")
        ex.tap(tx, ty)
        time.sleep(3)  # 点击后先等页面响应（验证码处理后/网络慢时切换慢）

        # 检测是否进入商品详情页（验证码检测优先——验证码页文字块少，不能被"加载中"分支挡掉）
        for _ in range(8):
            time.sleep(2)
            detail_check = _pfx("detail_check.png")
            ex.driver.screenshot(detail_check)
            detail_raw = OCR.recognize(detail_check, detail="all")
            detail_texts = [r["text"] for r in detail_raw]

            # 1. 验证码优先检测：验证码页文字块少（可能≤8），必须先于"加载中"判断
            captcha_kw = any("拖动滑块" in t or "请按住滑块" in t or "安全验证" in t for t in detail_texts)
            if captcha_kw:
                print(f"    ⚠ 检测到验证码页面，尝试自动处理...")
                if _handle_captcha(ex, detail_texts):
                    continue
                return "__TERMINATE__"

            # 1.5 「重新加载」页（出错了/检修中，验证码通过后常见）：点重新加载后继续等
            if any("重新加载" in t for t in detail_texts):
                for r in detail_raw:
                    if "重新加载" in r["text"]:
                        box = r["bbox"]
                        bx = (box[0][0] + box[2][0]) // 2
                        by = (box[0][1] + box[2][1]) // 2
                        print(f"    检测到「重新加载」页，点击重新加载 ({bx},{by})")
                        ex.tap(bx, by)
                        time.sleep(2)
                        break
                continue

            # 2. 页面加载中/切换中（文字少且无验证码）→ 继续等待，不误判
            if len(detail_texts) <= 8:
                print(f"    页面未就绪（仅{len(detail_texts)}块文字），继续等待...")
                continue

            # 2.5 检测是否退回首页/列表页（点标题失败/back过头时常见）→ 立即处理，不盲等
            joined_texts = "".join(detail_texts)
            if ("看病买药" in joined_texts) and ("我的" in joined_texts):
                print("    ⚠ 检测到已退回首页，触发重启恢复")
                return "__RESTART__"
            if "筛选" in joined_texts:
                print("    ⚠ 检测到已回列表页，放弃本店（点标题未成功进店）")
                return ""

            # 3. 检测商品详情页关键词
            if any("加入购物车" in t or "立即购买" in t or "选规格" in t or "商品详情页" in t for t in detail_texts):
                print(f"    已进入商品详情页")
                entered_detail = True
                break

            # 4. 不在详情页，检测是否还在店铺页
            has_ganggang = any("刚刚搜过" in t for t in detail_texts)
            has_pingjia = any("评价" in t for t in detail_texts)
            if has_ganggang and has_pingjia:
                print(f"    仍在店铺页，重试...")
                break  # 跳出内层循环

        if entered_detail:
            break

        # 内层循环结束仍未进入详情页：AI判断当前实际页面类型（诊断 + 验证码/首页/列表页兜底）
        try:
            last_shot = _pfx("detail_check.png")
            if cv2.imread(last_shot) is not None:
                ai_raw = OCR.recognize(last_shot, detail="all")
                ai_page = AIParser().check_page(ai_raw)
                print(f"    AI页面类型: {ai_page.get('type')} - {ai_page.get('detail', '')[:40]}")
                if ai_page.get("type") == "risk":
                    if not _has_captcha_kw([r["text"] for r in ai_raw]):
                        print("    AI判risk但OCR无验证码特征词，忽略")
                    else:
                        print("    ⚠ AI检测到验证码页，尝试自动处理...")
                        if _handle_captcha(ex, [r["text"] for r in ai_raw]):
                            continue
                        return "__TERMINATE__"
                if ai_page.get("type") == "home":
                    print("    ⚠ AI检测到首页，触发重启恢复")
                    return "__RESTART__"
                if ai_page.get("type") == "list":
                    print("    ⚠ AI检测到列表页，放弃本店")
                    return ""
                if ai_page.get("type") == "login":
                    ACCOUNT_ABORTED = True  # 本函数已声明global
                    print("    ⚠ AI检测到登录页（账号被踢/封号），停止采集")
                    return "__TERMINATE__"
        except Exception as e:
            print(f"    ⚠ AI页面类型检测异常: {e}")

        # 第1次失败后，重新OCR+AI获取坐标（可能是页面滚动导致坐标偏移）
        if attempt < 2:
            print(f"    重新OCR获取坐标...")
            re_shot = _pfx("shop.png")
            ex.driver.screenshot(re_shot)
            re_raw = OCR.recognize(re_shot, detail="all")
            re_sorted = sorted(re_raw, key=lambda r: r["bbox"][0][1])
            re_lines = []
            for r in re_sorted:
                r_cx = (r["bbox"][0][0] + r["bbox"][2][0]) // 2
                r_cy = (r["bbox"][0][1] + r["bbox"][2][1]) // 2
                re_lines.append(f"[x={r_cx:4d}, y={r_cy:4d}] {r['text']}")
            re_ocr_text = "\n".join(re_lines)
            re_resp = parser._call(system_prompt, f"商品标题: {product_title}\n\nOCR文字：\n{re_ocr_text}\n\n请返回商品标题坐标。")
            re_cleaned = re_resp.strip()
            if re_cleaned.startswith("```"):
                rl = re_cleaned.split("\n")
                if rl[0].startswith("```"): rl = rl[1:]
                if rl and rl[-1].strip() == "```": rl = rl[:-1]
                re_cleaned = "\n".join(rl).strip()
            try:
                re_data = json.loads(re_cleaned)
                re_xy = re_data.get("title_xy")
                if re_xy and isinstance(re_xy, list) and len(re_xy) == 2 and re_xy[0] is not None:
                    tx, ty = int(re_xy[0]), int(re_xy[1])
                    ww, hh = ex.driver.window_size()
                    if not (0 <= tx <= ww and 0 <= ty <= hh):
                        print(f"    ⚠ 新坐标越界: ({tx},{ty})，保持原坐标")
                    else:
                        print(f"    新坐标: ({tx},{ty})")
            except Exception:
                pass
    else:
        pass  # 3次重试结束

    if not entered_detail:
        print(f"    ⚠ 3次点击未进入商品详情页，跳过")
        return ""

    # ── 第3步：ORB特征匹配找分享图标 ──
    share_shot = _pfx("find_share.png")
    ex.driver.screenshot(share_shot)
    screen = cv2.imread(share_shot)
    template_path = str(Path(__file__).parent / "files" / "share.png")
    template = cv2.imread(template_path)
    sx, sy = None, None

    if screen is not None and template is not None:
        h_s, w_s = screen.shape[:2]

        # 右上角区域（分享图标永远在右上）
        roi_x1, roi_y1 = w_s * 2 // 3, 0
        roi = screen[roi_y1:h_s // 4, roi_x1:w_s]

        # 方法A: SIFT 特征匹配（限制右上角区域，减少干扰）
        sx, sy = None, None
        sift = cv2.SIFT_create(nfeatures=1500)
        kp1, des1 = sift.detectAndCompute(template, None)
        kp2, des2 = sift.detectAndCompute(roi, None)

        if des1 is not None and des2 is not None and len(kp1) >= 2 and len(kp2) >= 2:
            bf = cv2.BFMatcher()
            matches = bf.knnMatch(des1, des2, k=2)
            good = []
            for m, n in matches:
                if m.distance < 0.75 * n.distance:
                    good.append(m)
            print(f"    分享SIFT(右上区域): 模板{len(kp1)}特征 ROI{len(kp2)}特征 优质{len(good)}")
            if len(good) >= 4:
                src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
                dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
                matrix, _ = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
                if matrix is not None:
                    h_t, w_t = template.shape[:2]
                    corners = np.float32([[0, 0], [w_t, 0], [w_t, h_t], [0, h_t]]).reshape(-1, 1, 2)
                    transformed = cv2.perspectiveTransform(corners, matrix)
                    sx = roi_x1 + int(np.mean(transformed[:, 0, 0]))
                    sy = int(np.mean(transformed[:, 0, 1]))
                    print(f"    分享SIFT匹配: ({sx},{sy})")

        # 方法B: 多尺度模板匹配（右上角区域）
        # 分辨率适配：以 720p 为基准，按屏幕宽度比例调整搜索尺度，覆盖 480p~1080p
        base_w = 720.0
        scale_ratio = w_s / base_w
        # 模板在 720p 下约占 11% 屏宽，目标尺度应使模板覆盖相同比例
        scales = [round(scale_ratio * s, 2) for s in [0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5]]
        if sx is None:
            best_val, best_loc, best_sw, best_sh = 0, None, 0, 0
            for scale in scales:
                scaled = cv2.resize(template, None, fx=scale, fy=scale)
                sw, sh = scaled.shape[1], scaled.shape[0]
                if sh > roi.shape[0] or sw > roi.shape[1]:
                    continue
                res = cv2.matchTemplate(roi, scaled, cv2.TM_CCOEFF_NORMED)
                _, mv, _, ml = cv2.minMaxLoc(res)
                if mv > best_val:
                    best_val, best_loc, best_sw, best_sh = mv, ml, sw, sh
                t_edge = cv2.Canny(scaled, 30, 100)
                r_edge = cv2.Canny(roi, 30, 100)
                if t_edge.shape[0] <= r_edge.shape[0] and t_edge.shape[1] <= r_edge.shape[1]:
                    res2 = cv2.matchTemplate(r_edge, t_edge, cv2.TM_CCOEFF_NORMED)
                    _, mv2, _, ml2 = cv2.minMaxLoc(res2)
                    if mv2 > best_val:
                        best_val, best_loc, best_sw, best_sh = mv2, ml2, sw, sh
            print(f"    分享模板匹配(右上): 最佳={best_val:.3f}")
            if best_val >= 0.26 and best_loc is not None:
                sx = roi_x1 + best_loc[0] + best_sw // 2
                sy = best_loc[1] + best_sh // 2

        if sx is not None and sy is not None:
            print(f"    分享图标: ({sx},{sy})")
            ex.tap(sx, sy)
        else:
            print(f"    ⚠ 未找到分享图标")
            return ""

    # 等弹窗出现，同时记录"分享到"y坐标用于QR裁剪
    share_y = None
    waimai_y = None
    for _ in range(8):
        time.sleep(1)
        ck = _pfx("share_popup.png")
        ex.driver.screenshot(ck)
        detail = OCR.recognize(ck, detail="all")
        texts = [r["text"] for r in detail]
        if any("分享到" in t for t in texts):
            print(f"    分享弹窗出现")
            # 记录"分享到"和"外卖"的y坐标
            for r in detail:
                cy = (r["bbox"][0][1] + r["bbox"][2][1]) // 2
                if "分享到" in r["text"] and share_y is None:
                    share_y = cy
                if "外卖" in r["text"] and waimai_y is None:
                    waimai_y = cy
            break
    if share_y is None:
        share_y = int(ex.driver.window_size()[1] * 0.74)  # fallback

    # ── 第4步：截图 → 多方法解析二维码（多次重试） ──
    import os as _os_debug
    _debug_dir = str(SCREENSHOT_DIR / ex.device_id / "step4" / "debug_qr")
    _os_debug.makedirs(_debug_dir, exist_ok=True)
    # 微信QR解码器（对ECI编码的饿了么QR鲁棒，实测成功率94%）
    _wx_detector = cv2.wechat_qrcode.WeChatQRCode() if hasattr(cv2, "wechat_qrcode") else None

    def _decode_qr(img, share_y, waimai_y=None):
        """基于OCR定位的share_y裁剪QR区域解析"""
        if img is None: return ""
        h, w = img.shape[:2]
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        detector = cv2.QRCodeDetector()

        # 裁剪区域：y从"分享到"上方推算(不依赖"外卖"文字，避免OCR误判)
        # QR 通常在"分享到"上方 15%~35% 屏高范围，取中间偏下
        y_top = max(0, share_y - int(h * 0.30))
        y_bot = share_y
        x_l, x_r = int(w * 0.58), int(w * 0.94)
        crop_save = img[y_top:y_bot, x_l:x_r]
        cv2.imwrite(_pfx("qr_crop.png"), crop_save)

        # 保存调试截图：标注裁剪区域 + QR 边界（按设备ID区分，方便对比不同分辨率）
        debug_img = img.copy()
        cv2.rectangle(debug_img, (x_l, y_top), (x_r, y_bot), (0, 255, 0), 3)
        cv2.putText(debug_img, f"crop:({x_l},{y_top})~({x_r},{y_bot})", (x_l, y_top - 10),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
        _ts = int(time.time())
        cv2.imwrite(f"{_debug_dir}/{ex.device_id}_qr_region_{_ts}.png", debug_img)

        # 方法0: 微信QR解码器(对ECI编码的饿了么QR鲁棒，实测94%成功率，全图直接解析)
        if _wx_detector is not None:
            try:
                wx_texts, wx_points = _wx_detector.detectAndDecode(img)
                if wx_texts and wx_texts[0]:
                    data = wx_texts[0]
                    if wx_points is not None and len(wx_points) > 0:
                        wp = wx_points[0].astype(int)
                        x1, y1 = wp[:, 0].min(), wp[:, 1].min()
                        x2, y2 = wp[:, 0].max(), wp[:, 1].max()
                        cv2.rectangle(debug_img, (x1, y1), (x2, y2), (0, 0, 255), 3)
                        cv2.putText(debug_img, "WX-QR", (x1, y1 - 10),
                                    cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
                    cv2.imwrite(f"{_debug_dir}/{ex.device_id}_qr_found_{_ts}.png", debug_img)
                    return data
            except Exception:
                pass

        def _try_decode(roi_gray, zooms=(1,)):
            """在灰度图上尝试多种方式解码"""
            if roi_gray is None or roi_gray.size == 0 or roi_gray.shape[0] == 0 or roi_gray.shape[1] == 0:
                return ""
            # 高分辨率下 QR 可能太大导致 detect 失败,先缩到合理尺寸再解析
            h_roi, w_roi = roi_gray.shape[:2]
            if max(h_roi, w_roi) > 500:
                scale_down = 500 / max(h_roi, w_roi)
                roi_small = cv2.resize(roi_gray, None, fx=scale_down, fy=scale_down, interpolation=cv2.INTER_AREA)
            else:
                roi_small = roi_gray
            for z in zooms:
                if z > 1:
                    big = cv2.resize(roi_small, None, fx=z, fy=z, interpolation=cv2.INTER_NEAREST)
                else:
                    big = roi_small
                data, _, _ = detector.detectAndDecode(big)
                if data: return data
            # OTSU + zoom
            for z in zooms:
                _, th = cv2.threshold(roi_small, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
                big = cv2.resize(th, None, fx=z, fy=z, interpolation=cv2.INTER_NEAREST) if z > 1 else th
                data, _, _ = detector.detectAndDecode(big)
                if data: return data
            return ""

        # 方法A: 全图detect定位QR → 中心 ±qr_half 精确裁剪解析（qr_half 随分辨率缩放）
        # QR 在全图里 detect 可能失败（干扰太多），但先试试
        ok, points = detector.detect(gray)
        if ok and points is not None and len(points) > 0:
            pts = points[0].astype(int)
            cx = int(np.mean(pts[:, 0]))
            cy = int(np.mean(pts[:, 1]))
            # 根据 detect 到的 QR 边界估算大小,裁剪 QR 中心 ± qr_half
            qr_half = max(int(max(np.linalg.norm(pts[0] - pts[1]), np.linalg.norm(pts[1] - pts[2])) / 2) + 20, 60)
            x1, y1 = max(0, cx - qr_half), max(0, cy - qr_half)
            x2, y2 = min(w, cx + qr_half), min(h, cy + qr_half)
            if x2 > x1 and y2 > y1:
                data = _try_decode(gray[y1:y2, x1:x2], (1, 2, 3))
                if data:
                    # 在调试截图上标注 QR 检测位置
                    cv2.rectangle(debug_img, (x1, y1), (x2, y2), (0, 0, 255), 3)
                    cv2.putText(debug_img, f"QR:({cx},{cy})", (x1, y1 - 10),
                                cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
                    cv2.imwrite(f"{_debug_dir}/{ex.device_id}_qr_found_{_ts}.png", debug_img)
                    return data

        # 方法B: 用 detector.detect 在扫描区域内定位 QR → 中心 ± qr_half 精确裁剪解析
        scan_area = gray[y_top:y_bot, x_l:x_r]
        sh, sw = scan_area.shape
        ok2, pts2 = detector.detect(scan_area)
        if ok2 and pts2 is not None and len(pts2) > 0:
            qr_pts = pts2[0].astype(int)
            qx = int(np.mean(qr_pts[:, 0]))
            qy = int(np.mean(qr_pts[:, 1]))
            qr_half = max(int(max(np.linalg.norm(qr_pts[0] - qr_pts[1]), np.linalg.norm(qr_pts[1] - qr_pts[2])) / 2) + 20, 60)
            qx1, qy1 = max(0, qx - qr_half), max(0, qy - qr_half)
            qx2, qy2 = min(sw, qx + qr_half), min(sh, qy + qr_half)
            if qx2 > qx1 and qy2 > qy1:
                data = _try_decode(scan_area[qy1:qy2, qx1:qx2], (1, 2, 3))
                if data:
                    cv2.rectangle(debug_img, (x_l + qx1, y_top + qy1), (x_l + qx2, y_top + qy2), (0, 0, 255), 3)
                    cv2.putText(debug_img, f"QR:({x_l + qx},{y_top + qy})", (x_l + qx1, y_top + qy1 - 10),
                                cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
                    cv2.imwrite(f"{_debug_dir}/{ex.device_id}_qr_found_{_ts}.png", debug_img)
                    return data

        # 方法C: 滑动窗口扫描（基于OCR定位区域，尺寸随分辨率缩放）
        win = max(40, min(200, sh // 2, sw // 2))
        step = max(40, win // 3)
        for y in range(0, max(1, sh - win), step):
            for x in range(0, max(1, sw - win), step):
                patch = scan_area[y:y+win, x:x+win]
                data = _try_decode(patch, (1, 2))
                if data:
                    # 在调试截图上标注命中的窗口位置
                    cv2.rectangle(debug_img, (x_l + x, y_top + y), (x_l + x + win, y_top + y + win), (0, 0, 255), 3)
                    cv2.imwrite(f"{_debug_dir}/{ex.device_id}_qr_found_{_ts}.png", debug_img)
                    return data

        # 方法D: 全图 OTSU + detect → 中心 ± qr_half 精确裁剪解析
        _, full_th = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
        ok3, pts3 = detector.detect(full_th)
        if ok3 and pts3 is not None and len(pts3) > 0:
            qr_pts = pts3[0].astype(int)
            qx = int(np.mean(qr_pts[:, 0]))
            qy = int(np.mean(qr_pts[:, 1]))
            qr_half = max(int(max(np.linalg.norm(qr_pts[0] - qr_pts[1]), np.linalg.norm(qr_pts[1] - qr_pts[2])) / 2) + 20, 60)
            qx1, qy1 = max(0, qx - qr_half), max(0, qy - qr_half)
            qx2, qy2 = min(w, qx + qr_half), min(h, qy + qr_half)
            if qx2 > qx1 and qy2 > qy1:
                data = _try_decode(gray[qy1:qy2, qx1:qx2], (1, 2, 3))
                if data:
                    cv2.rectangle(debug_img, (qx1, qy1), (qx2, qy2), (0, 0, 255), 3)
                    cv2.putText(debug_img, f"QR:({qx},{qy})", (qx1, qy1 - 10),
                                cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
                    cv2.imwrite(f"{_debug_dir}/{ex.device_id}_qr_found_{_ts}.png", debug_img)
                    return data

        return ""

    for retry in range(6):  # 最多等 5+2*5=15秒
        time.sleep(5 if retry == 0 else 2)
        qr_shot = _pfx("qr.png")
        ex.driver.screenshot(qr_shot)
        data = _decode_qr(cv2.imread(qr_shot), share_y, waimai_y)
        if data:
            print(f"    QR链接: {data[:100]}")
            return data

    return ""


# ── 步骤 3：滑动 + 逐个点击店铺 ──────────────────────

def _progress_file_path(device_id: str, keyword: str) -> str:
    """进度文件路径（美团同款：ycwj/{设备}_{药品}.txt）"""
    import hashlib
    safe = hashlib.md5(keyword.encode()).hexdigest()[:8]
    return str(Path(__file__).parent / "ycwj" / f"{device_id}_{safe}.txt")


def _save_progress(device_id: str, keyword: str, visited: set, scroll_px: int, batch_no: int) -> None:
    """保存采集进度（每批滑动后调用，异常退出时进度已在）"""
    import os
    try:
        path = _progress_file_path(device_id, keyword)
        os.makedirs(os.path.dirname(path), exist_ok=True)
        data = {
            "visited": sorted(visited),
            "scroll_px": scroll_px,
            "batch_no": batch_no,
            "time": time.strftime("%Y-%m-%d %H:%M:%S"),
        }
        with open(path, "w", encoding="utf-8") as f:
            json.dump(data, f, ensure_ascii=False, indent=2)
    except Exception as e:
        print(f"[step3] 保存进度失败: {e}")


def _load_progress(device_id: str, keyword: str):
    """读取采集进度，无进度文件返回 None"""
    import os
    try:
        path = _progress_file_path(device_id, keyword)
        if not os.path.exists(path):
            return None
        with open(path, "r", encoding="utf-8") as f:
            return json.load(f)
    except Exception as e:
        print(f"[step3] 读取进度失败: {e}")
        return None


def _delete_progress(device_id: str, keyword: str) -> None:
    """任务正常完成后删除进度文件"""
    import os
    try:
        path = _progress_file_path(device_id, keyword)
        if os.path.exists(path):
            os.remove(path)
            print(f"[step3] 进度文件已删除: {path}")
    except Exception as e:
        print(f"[step3] 删除进度失败: {e}")


def _spec_ok(title: str, spec_list: list) -> bool:
    """
    标题是否包含任一目标规格（美团 is_link_spec_useful 同款）。
    规格是数字+单位，OCR对数字错误率极低，用精确匹配（模糊匹配会把"10袋"误配到"10g"）。
    """
    if not spec_list:
        return True
    nt = normalize_match_text(title)
    return any(normalize_match_text(s) in nt for s in spec_list)


def step3_swipe_and_enter(ex: SafeExecutor, keyword: str, brand: str = "", task: dict = None,
                          scheduler=None) -> list:
    """
    截图 → AI分析 → 逐个点击全部可见店铺 → 下滑加载更多 → 继续点击 → 直到全部遍历

    品牌+药品名过滤（美团 is_link_useful 同款）：标题必须同时包含品牌名和药品名，
    否则过滤；连续30个无关商品则任务结束停止采集。
    task: 调度任务 dict（task_id/enterprise_id/collect_round/current_page等，手动模式传 None）
    scheduler: 调度器（逐页回告进度，手动模式传 None）
    """
    print("\n" + "=" * 40)
    print("  步骤 3：遍历店铺")
    print("=" * 40)

    # 新任务开始：重置所有停止标志和缓存（上个任务的验证码/封号停止不能污染本任务）
    global _CLOSE_BTN_CACHE, CAPTCHA_ABORTED, CAPTCHA_ABORT_REASON, ACCOUNT_ABORTED, CURRENT_PAGE
    global _SCHEDULER, _TASK_ID, _CRAWLED_COUNT
    _CLOSE_BTN_CACHE = None
    CAPTCHA_ABORTED = False
    CAPTCHA_ABORT_REASON = ""
    ACCOUNT_ABORTED = False
    CURRENT_PAGE = 0
    _SCHEDULER = scheduler
    _TASK_ID = (task or {}).get("task_id")
    _CRAWLED_COUNT = 0

    w, h = ex.driver.window_size()
    batch_no = 0
    empty_streak = 0  # 连续没有新店铺的批次数
    all_results = []
    unrelated = 0     # 连续无关商品计数（美团同款；>=30 停止采集）
    n_brand = normalize_match_text(brand)
    # 目标规格（调度任务/手动 --spec 传入，如 "120粒|60粒"），用于规格过滤+入库
    spec_raw = str((task or {}).get("product_specs") or "")
    spec_list = (task or {}).get("spec_list") or []
    if isinstance(spec_list, str):  # 兼容直接传字符串
        spec_list = [s.strip() for s in re.split(r'[|、，,\n\r]+', spec_list) if s.strip()]
    n_key = normalize_match_text(keyword)
    stopped = False

    # 恢复进度：
    # 1. 跨设备接力：task 带 current_page（调度重派时给）→ 按页码滑动恢复（每页=0.7屏高，跨分辨率一致）
    # 2. 同设备异常恢复：本地进度文件（visited + 滑动px，精确恢复）
    start_page = int((task or {}).get("current_page") or 0)
    progress = _load_progress(ex.device_id, keyword)
    visited = set()
    total_scroll_px = 0
    if start_page > 0:
        print(f"[step3] 跨设备接力恢复: 调度页码={start_page}，滑动{start_page}页...")
        for i in range(start_page):
            _adb_swipe_up(ex, int(h * 0.7))
        time.sleep(2)
    elif progress:
        visited = set(progress.get("visited") or [])
        total_scroll_px = int(progress.get("scroll_px") or 0)
        print(f"[step3] 恢复进度: 已访问{len(visited)}个店铺，需滑动恢复{total_scroll_px}px")
        remain = total_scroll_px
        while remain > 0:
            step = min(remain, int(h * 0.5))
            _adb_swipe_up(ex, step)
            remain -= step
        time.sleep(2)
    else:
        visited = set()

    MAX_RESTART = 3   # unknown页面异常时最大重启恢复次数（超过则终止任务）
    restart_count = 0

    while True:
        if CAPTCHA_ABORTED:
            print(f"[step3] {CAPTCHA_ABORT_REASON or chr(39)+chr(39)}停止采集")
            break
        _check_account_kicked(ex)  # 每批检测账号是否被踢/封号（xpath检查，开销小）
        if ACCOUNT_ABORTED:
            print("[step3] 账号被踢/封号，停止采集")
            break
        named, raw_local = _get_named_shops(ex, f"step3_b{batch_no}.png", keyword)
        if _ai_check_captcha(ex):  # 列表页每批检测验证码（风控弹窗可能出现在列表）
            time.sleep(1)
            named, raw_local = _get_named_shops(ex, f"step3_b{batch_no}.png", keyword)  # 验证码处理后重新识别
        raw_new = [s for s in named if _shop_key(s) not in visited]

        # 品牌+药品名+规格过滤（测试OCR准确率时可关闭：ENABLE_TITLE_FILTER=False 直接采集全部结果）
        if not ENABLE_TITLE_FILTER:
            # 测试模式：不过滤，直接采集OCR识别到的全部结果
            new_ones = list(raw_new)
        else:
            cloud_cache = {"done": False, "texts": None}
            shot_path = _shot_path(ex, f"step3_b{batch_no}.png")
            new_ones = []
            for s in raw_new:
                # 裁剪确认用标题块自己的坐标（卡片中心裁剪会漏掉标题行）
                title_y = _find_title_y(raw_local, str(s[1] or ""))
                if title_y is None and len(s) > 3 and s[3]:
                    title_y = int(s[3][1])
                v, v_reason = _match_verify(str(s[1] or ""), n_brand, n_key, shot_path, cloud_cache,
                                            card_y=title_y)
                if v in ("ok", "fuzzy"):
                    # 规格过滤（美团 is_link_spec_useful 同款）：标题需包含任一目标规格
                    if spec_list and not _spec_ok(str(s[1] or ""), spec_list):
                        unrelated += 1
                        print(f"[step3] 过滤: {str(s[1])[:30]} 不含目标规格{spec_list} (连续{unrelated}个无关)")
                        if unrelated >= 30:
                            print(f"[step3] 连续{unrelated}个非目标商品，任务结束停止采集")
                            stopped = True
                            break
                        continue
                    unrelated = 0
                    # 云端验证过：生僻字修正开关开启时才用云端文本修正标题错字（默认关闭，标题保持OCR原样）
                    if v == "ok" and ENABLE_RARE_CHAR_CORRECT and cloud_cache["texts"] is not None:
                        s = list(s)
                        s[1] = _correct_title_with_cloud(str(s[1]), n_brand, n_key, cloud_cache["texts"])
                    new_ones.append(s)
                else:
                    unrelated += 1
                    print(f"[step3] 过滤: {str(s[1])[:30]} 不含目标{v_reason} (连续{unrelated}个无关)")
                    if unrelated >= 30:
                        print(f"[step3] 连续{unrelated}个非目标商品，任务结束停止采集")
                        stopped = True
                        break
        if stopped:
            print("[step3] 停止采集，返回已采结果")
            break

        print(f"[step3] 批次{batch_no}: 共{len(named)}个, 新{len(raw_new)}个, 通过过滤{len(new_ones)}个")

        # 逐页回告调度：页码 = 起点页码 + 批次数（每滑一屏算一页；global已在任务开头声明）
        CURRENT_PAGE = start_page + batch_no
        _CRAWLED_COUNT = len(all_results)
        if scheduler is not None:
            try:
                scheduler.post_report({
                    "task_id": (task or {}).get("task_id"),
                    "platform": scheduler.platform,
                    "username": scheduler.username,
                    "is_finished": 0,
                    "need_reassign": 0,
                    "current_page": start_page + batch_no,
                    "crawled_count": len(all_results),
                })
            except Exception as e:
                print(f"[step3] 逐页回告失败: {e}")

        if not new_ones:
            empty_streak += 1
            print(f"[step3] 无新店铺 (连续{empty_streak}/3)")
            if empty_streak >= 3:
                print(f"[step3] 连续3批无新店铺，结束")
                break
            # 滑动后再试
            print(f"[step3] 滑动查看下一批")
            if len(named) >= 2:
                target_y = named[-2][4]
                swipe_dist = max(target_y - int(h * 0.15), int(h * 0.15))
            else:
                swipe_dist = int(h * 0.3)
            _adb_swipe_up(ex, swipe_dist)
            total_scroll_px += swipe_dist
            time.sleep(2)
            batch_no += 1
            _save_progress(ex.device_id, keyword, visited, total_scroll_px, batch_no)
            continue

        empty_streak = 0  # 有新店铺，重置计数

        restart_requested = False
        for shop in new_ones:
            result = _visit_shop(ex, shop, visited, keyword, task)
            if result and result.get("__terminate__"):
                print("[step3] 收到终止信号，停止遍历")
                all_results = [r for r in all_results if not r.get("__terminate__")]
                stopped = True
                break
            if result and result.get("__restart__"):
                restart_requested = True
                break
            if result:
                result["brand"] = brand
                result["product_specs"] = spec_raw
                all_results.append(result)
                save_record(result)  # 每采完一个立即入库，中断不丢数据

        if restart_requested:
            # unknown页面异常：重启App恢复（进度按页保存，visited保留在内存）
            restart_count += 1
            if restart_count >= MAX_RESTART:
                print(f"[step3] 重启恢复超限({MAX_RESTART}次)，终止任务")
                stopped = True
                break
            print(f"[step3] unknown页面异常，重启App恢复(第{restart_count}/{MAX_RESTART}次)")
            _save_progress(ex.device_id, keyword, visited, total_scroll_px, batch_no)
            step1_open_app(ex)
            step2_search(ex, (brand + keyword).strip() or keyword)
            # 从列表顶部滑动恢复到上次位置
            remain = total_scroll_px
            while remain > 0:
                step = min(remain, int(h * 0.5))
                _adb_swipe_up(ex, step)
                remain -= step
            time.sleep(2)
            continue

        if stopped:
            break

        # 正常：滑动到倒数第二个卡片的配送距离位置，进入下一批
        print(f"[step3] 已访问 {len(visited)} 个，滑动查看下一批")
        if len(named) >= 2:
            target_y = named[-2][4]  # 倒数第二个卡片的配送距离y坐标
            swipe_dist = max(target_y - int(h * 0.15), int(h * 0.15))
        else:
            swipe_dist = int(h * 0.3)
        _adb_swipe_up(ex, swipe_dist)
        total_scroll_px += swipe_dist
        time.sleep(2)
        batch_no += 1
        _save_progress(ex.device_id, keyword, visited, total_scroll_px, batch_no)  # 每页保存一次进度
        continue

    # 任务结束：只有正常完成才删除进度文件；验证码/封号等异常停止时保留（续采位置不丢）
    if not CAPTCHA_ABORTED and not ACCOUNT_ABORTED:
        _delete_progress(ex.device_id, keyword)
    else:
        print(f"[step3] 异常停止，保留进度文件以便恢复: {_progress_file_path(ex.device_id, keyword)}")

    # ── 输出最终结果表 ──
    print("\n" + "=" * 70)
    print(f"  最终结果 ({len(all_results)} 个店铺)")
    print("=" * 70)
    for i, r in enumerate(all_results, 1):
        link_short = r["link"][:55] + "..." if len(r["link"]) > 55 else r["link"]
        print(f"  [{i}] {r['shop']}")
        print(f"      商品: {r['title'][:40]}")
        print(f"      价格: {r['price']}")
        print(f"      月售: {r.get('sales', '')}")
        print(f"      批准文号: {r.get('approval_no', '')}")
        print(f"      有效期: {r.get('validity', '')}")
        print(f"      资质编号: {r.get('license_no', '')}")
        lic = r.get("license") or {}
        print(f"      执照: {lic.get('单位名称', '')} 信用代码:{lic.get('社会信用代码', '')} 法人:{lic.get('法人', '')}")
        print(f"      执照地址: {lic.get('地址', '')[:40]}")
        print(f"      链接: {link_short}")
        print()

    return all_results


# ── 步骤 4：（保留，当前为空 ──────────────────────────

def step4_empty(ex: SafeExecutor):
    """占位，供后续扩展"""
    pass


# ── 主入口 ──────────────────────────────────────────────

if __name__ == "__main__":
    # 启动时自动清理：debug_qr 调试图只保留1天
    try:
        for p in SCREENSHOT_DIR.glob("*/step4/debug_qr/*.png"):
            if time.time() - p.stat().st_mtime > 86400:
                p.unlink(missing_ok=True)
    except Exception:
        pass

    args = sys.argv[1:]
    device_id = "RG5LFYT8UKK7BI95"
    brand = "三九胃泰"
    keyword = "养胃舒颗粒"
    spec_raw = ""
    spec_list = []
    # 解析 --device / --brand / --keyword / --spec 参数（品牌和药品名分开传，对接调度系统）
    filtered = []
    i = 0
    while i < len(args):
        if args[i] == "--device" and i + 1 < len(args):
            device_id = args[i + 1]
            i += 2
        elif args[i] == "--brand" and i + 1 < len(args):
            brand = args[i + 1]
            i += 2
        elif args[i] == "--keyword" and i + 1 < len(args):
            keyword = args[i + 1]
            i += 2
        elif args[i] == "--spec" and i + 1 < len(args):
            spec_raw = args[i + 1]
            spec_list = [s.strip() for s in re.split(r'[|、，,\n\r]+', spec_raw) if s.strip()]
            i += 2
        else:
            filtered.append(args[i])
            i += 1

    cmd = filtered[0] if filtered else "all"
    if not keyword:
        keyword = filtered[1] if len(filtered) > 1 else "矿泉水"
    # 搜索词 = 品牌+药品名+规格 合起来（美团同款：分开配置，搜索时合并）
    search_key = (brand + keyword + spec_raw).strip() or keyword
    print(f"品牌: {brand or '(无)'} | 药品名: {keyword} | 规格: {spec_list or '(不限)'} | 搜索词: {search_key}")

    print("设备连接中...")
    device_id = _find_device(device_id)
    print(f"设备: {device_id}")
    ex = SafeExecutor(device_id)

    if cmd in ("all", "step1"):
        ok = step1_open_app(ex)
        if not ok:
            sys.exit(1)

    if cmd in ("all", "step2"):
        ok = step2_search(ex, search_key)
        if not ok:
            sys.exit(1)

    if cmd in ("all", "step3"):
        task = {"spec_list": spec_list, "product_specs": spec_raw}
        visited = step3_swipe_and_enter(ex, keyword, brand, task)
        print(f"\n最终访问: {visited}")
        sys.exit(0)

    if cmd in ("all", "step4"):
        # step4 需要先跑完 step3 获取所有商品标题，单独跑时需要手动传标题
        title = keyword
        link = step4_parse_qr(ex, title)
        print(f"\n链接: {link}")
        sys.exit(0)

    sys.exit(0)