""" analyzer.py — 录制步骤差异分析引擎 (v1.0) 三阶段管线 Phase 2: 读取录制包 → 对比相邻步骤的 URL/DOM/事件 → 推断操作类型 → 生成结构化分析结果 用法: from mc.analyzer import analyze_recording result = analyze_recording(package) """ import json import re from pathlib import Path from typing import Optional # ── 抖音快捷键映射 ── DY_HOTKEYS = { 'z': 'like', 'x': 'comment', 'c': 'comment', 'g': 'follow', 'k': 'loop', 'm': 'mute', 'j': 'clear', 'p': 'loop', 'h': 'fullscreen', 'ArrowDown': 'next_video', 'ArrowUp': 'prev_video', ' ': 'play_pause', } # ── 点击坐标区域定义 ── # (x_min, y_min, x_max, y_max, label) KNOWN_REGIONS = { 'dy_like_area': (330, 400, 500, 600, '抖音点赞区域'), 'dy_comment_area': (350, 680, 500, 720, '抖音评论区触发'), 'dy_search_bar': (50, 50, 350, 90, '抖音搜索框'), 'xhs_bottom_nav': (0, 700, 400, 780, 'XHS底部导航'), 'xhs_me_tab': (300, 700, 400, 780, 'XHS"我"标签'), } def analyze_recording(package: dict) -> dict: """分析录制包，生成可导出的原子操作定义""" steps = package.get("steps", []) if len(steps) < 2: return {"error": "至少需要2个步骤才能分析", "steps_analyzed": 0} result = { "meta": package.get("meta", {}), "steps_analyzed": len(steps), "actions": [], "blueprint_suggestions": [], } # 分析相邻步骤对 for i in range(len(steps) - 1): before = steps[i] after = steps[i+1] analysis = _analyze_step_pair(before, after) result["actions"].append(analysis) # 生成蓝图建议 result["blueprint_suggestions"] = _suggest_blueprint(result["actions"]) return result def _analyze_step_pair(before: dict, after: dict) -> dict: """分析一对相邻步骤的变化""" bp = before.get("page", {}) ap = after.get("page", {}) events = before.get("events", []) # before_state 对比（三段式记录的状态指纹） bbs = before.get("before_state", {}) or {} abs = after.get("before_state", {}) or {} analysis = { "step_before": before["step"], "step_after": after["step"], "event_count": len(events), "page_url": ap.get("url", ""), "page_mode": ap.get("page_mode", "?"), "interactable_count": ap.get("interactable_count", 0), } # 0. before_state 选择器变化分析（优先级最高） sel_before = bbs.get("selectors", {}) sel_after = abs.get("selectors", {}) if sel_before and sel_after: sel_changes = {} all_keys = set(sel_before) | set(sel_after) for k in all_keys: v1 = sel_before.get(k, False) v2 = sel_after.get(k, False) if v1 != v2: sel_changes[k] = f"{'✅' if v1 else '❌'}→{'✅' if v2 else '❌'}" if sel_changes: analysis["selector_changes"] = sel_changes # 基于选择器变化推断操作 op = _infer_op_from_selectors(sel_before, sel_after, bbs, abs) if op: analysis["action_type"] = op["type"] analysis["action_desc"] = op["desc"] analysis["confidence"] = op["confidence"] # 1. URL 变化分析 url_before = bp.get("url", "") url_after = ap.get("url", "") if url_before != url_after: analysis["url_changed"] = True analysis["url_from"] = url_before[:80] analysis["url_to"] = url_after[:80] # URL 变化类型推断 if "/video/" in url_after and "/video/" not in url_before: analysis["action_type"] = "navigate_video" analysis["action_desc"] = "进入视频播放页" elif "/user/profile/" in url_after: analysis["action_type"] = "navigate_profile" analysis["action_desc"] = "进入个人主页" elif "/search/" in url_after or "?keyword=" in url_after: analysis["action_type"] = "search" analysis["action_desc"] = "搜索" elif "/user/self" in url_after: analysis["action_type"] = "navigate_profile" analysis["action_desc"] = "进入抖音主页" else: analysis["action_type"] = "navigate" analysis["action_desc"] = "页面导航" else: analysis["url_changed"] = False # 2. 页面模式变化 mode_before = bp.get("page_mode", "?") mode_after = ap.get("page_mode", "?") if mode_before != mode_after: analysis["mode_changed"] = f"{mode_before} → {mode_after}" if mode_before == "grid" and mode_after == "player": analysis["action_type"] = analysis.get("action_type", "enter_video") analysis["action_desc"] = analysis.get("action_desc", "进入视频播放") elif mode_before == "player" and mode_after == "grid": analysis["action_type"] = analysis.get("action_type", "exit_video") analysis["action_desc"] = analysis.get("action_desc", "退出视频") elif mode_after == "profile": analysis["action_type"] = analysis.get("action_type", "view_profile") analysis["action_desc"] = analysis.get("action_desc", "查看主页") # 3. 事件序列分析（从用户操作反推操作类型） if events: action = _infer_action_from_events(events) if action: analysis["inferred_action"] = action # 事件推断的优先级高于 URL 推断 if action.get("type") and not analysis.get("action_type"): analysis["action_type"] = action["type"] analysis["action_desc"] = action.get("desc", "") # 4. 文本差异分析 text_before = bp.get("text_snippet", "") text_after = ap.get("text_snippet", "") if text_before and text_after and text_before != text_after: analysis["text_changed"] = True # 检测文本中的关键变化 added = _find_added_text(text_before, text_after) if added: analysis["text_added"] = added[:100] # 5. 标题变化 title_before = bp.get("title", "") title_after = ap.get("title", "") if title_before != title_after: analysis["title_changed"] = f"{title_before[:30]} → {title_after[:30]}" # 6. 交互元素变化 ia_before = set(e.get("e2e", "") or e.get("cls", "") for e in bp.get("interactables", [])) ia_after = set(e.get("e2e", "") or e.get("cls", "") for e in ap.get("interactables", [])) new_elements = ia_after - ia_before if new_elements: analysis["new_elements"] = list(new_elements)[:5] # 7. 生成特征码（可复现的自动化选择器建议） analysis["features"] = _suggest_features(bp, ap, events, analysis) # 8. 检测空步骤（连续两次 · 无操作） if not analysis.get("action_type"): no_events = len(events) <= 1 # 只有 · 键事件 no_url_change = not analysis.get("url_changed", True) no_sel_change = not analysis.get("selector_changes") no_mode_change = not analysis.get("mode_changed") no_clicks = len([e for e in events if e.get('t') == 'click']) == 0 no_scrolls = len([e for e in events if e.get('t') == 'scroll']) == 0 if no_events and no_url_change and no_sel_change and no_mode_change: analysis["action_type"] = "skip" analysis["action_desc"] = "无有效操作（空步骤）" analysis["skip_reason"] = "no_change" # 9. 生成操作摘要 if not analysis.get("action_type"): analysis["action_type"] = "unknown" analysis["action_desc"] = f"页面内容变化 ({len(events)} events)" return analysis def _infer_op_from_selectors(sel_before: dict, sel_after: dict, bbs: dict, abs: dict) -> Optional[dict]: """基于 before_state 的选择器变化推断具体操作""" # 抖音 like 按钮: [data-e2e="video-player-digg"] # 抖音 collect: [data-e2e="video-player-collect"] # 小红书 note: section.note-item digg_key = '[data-e2e="video-player-digg"]' collect_key = '[data-e2e="video-player-collect"]' search_key = '[data-e2e="searchbar-input"]' video_key = 'video' note_key = 'section.note-item' p_before = bbs.get("page_mode", "") p_after = abs.get("page_mode", "") # 检测抖音点赞操作 digg_before = sel_before.get(digg_key, False) digg_after = sel_after.get(digg_key, False) if digg_before and not digg_after and p_before == "player": return {"type": "like", "desc": "点赞", "confidence": "high"} # 检测抖音取消点赞 if not digg_before and digg_after and p_before == "player": liked_text_before = bbs.get("markers", {}).get("liked", "") liked_text_after = abs.get("markers", {}).get("liked", "") return {"type": "like", "desc": "点赞（按钮重显）", "confidence": "medium"} # 检测抖音收藏操作 collect_before = sel_before.get(collect_key, False) collect_after = sel_after.get(collect_key, False) if collect_before and not collect_after and p_before == "player": return {"type": "collect", "desc": "收藏", "confidence": "high"} # 检测搜索操作 search_before = sel_before.get(search_key, False) search_after = sel_after.get(search_key, False) if not search_before and search_after: return {"type": "search", "desc": "打开搜索", "confidence": "high"} if search_before and not search_after: return {"type": "go_back", "desc": "关闭搜索", "confidence": "medium"} # 检测进入小红书笔记 note_before = sel_before.get(note_key, False) note_after = sel_after.get(note_key, False) if note_before and not note_after and p_before == "grid": return {"type": "xhs_click_note", "desc": "点击笔记", "confidence": "high"} if not note_before and note_after: return {"type": "go_back", "desc": "返回笔记列表", "confidence": "medium"} # 检测视频播放状态变化 video_before = sel_before.get(video_key, False) video_after = sel_after.get(video_key, False) if not video_before and video_after: return {"type": "enter_video", "desc": "进入视频页", "confidence": "medium"} if video_before and not video_after: return {"type": "exit_video", "desc": "退出视频", "confidence": "medium"} return None def _infer_action_from_events(events: list) -> Optional[dict]: """从 CDP 事件序列推断操作类型""" # 取关键事件：键盘 + 点击 key_events = [e for e in events if e.get('t') == 'key'] click_events = [e for e in events if e.get('t') == 'click'] scroll_events = [e for e in events if e.get('t') == 'scroll'] # 抖音快捷键识别 for ke in key_events: k = ke.get('k', '').lower() if k in DY_HOTKEYS: action = DY_HOTKEYS[k] desc_map = { 'like': '点赞', 'comment': '打开评论', 'follow': '关注', 'loop': '连播', 'mute': '静音', 'clear': '清屏', 'fullscreen': '全屏', 'next_video': '下一个视频', 'prev_video': '上一个视频', 'play_pause': '播放/暂停', } return {"type": action, "desc": desc_map.get(action, action), "trigger": f"key:{k}", "confidence": "high"} # 字母键识别 for ke in key_events: k = ke.get('k', '') if len(k) == 1 and k.isalpha(): return {"type": f"keypress_{k}", "desc": f"按键 {k}", "trigger": f"key:{k}", "confidence": "medium"} # 点击事件分析 if click_events: last_click = click_events[-1] x, y = last_click.get('x', 0), last_click.get('y', 0) target = last_click.get('target', '') # 检测区域 for region_name, (x1, y1, x2, y2, label) in KNOWN_REGIONS.items(): if x1 <= x <= x2 and y1 <= y <= y2: return {"type": f"click_{region_name}", "desc": label, "trigger": f"click({x},{y})", "confidence": "medium", "coords": {"x": x, "y": y}} return {"type": "click", "desc": f"点击 ({x},{y}) <{target}>", "trigger": f"click({x},{y})", "confidence": "medium", "coords": {"x": x, "y": y}} # 滚动事件 if scroll_events and len(scroll_events) >= 2: total_dy = sum(e.get('dy', 0) for e in scroll_events) if total_dy > 200: return {"type": "scroll", "desc": f"下滚 (delta={total_dy})", "trigger": "scroll", "confidence": "medium"} return None def _find_added_text(old_text: str, new_text: str) -> str: """简单的文本差异检测——找出出现在新文本中但不在旧文本中的部分""" # 行级别 diff old_lines = set(old_text.split('\n')) new_lines = new_text.split('\n') added = [l.strip() for l in new_lines if l.strip() and l.strip() not in old_lines and len(l.strip()) > 3] return '\n'.join(added[:10]) if added else '' def _suggest_features(before_page: dict, after_page: dict, events: list, analysis: dict) -> list: """从事件和页面状态生成自动化特征码建议""" features = [] key_events = [e for e in events if e.get('t') == 'key'] click_events = [e for e in events if e.get('t') == 'click'] # 来自推断操作的特征 inferred = analysis.get("inferred_action", {}) if inferred: trigger = inferred.get("trigger", "") if trigger.startswith("key:"): k = trigger.split(":")[1] features.append({ "type": "keyboard", "selector": None, "description": f"按键 {k}", "code": f'await page.keyboard.press("{k}")', "confidence": inferred.get("confidence", "medium"), }) elif "coords" in inferred: coords = inferred["coords"] # 尝试从 after_page 的 interactables 中找到最近的可点击元素 closest = None for el in after_page.get("interactables", []): rect_str = el.get("rect", "") if rect_str and ',' in rect_str: parts = rect_str.split() if len(parts) >= 1: xy = parts[0].split(',') if len(xy) == 2: try: ex, ey = int(xy[0]), int(xy[1]) dist = (coords['x'] - ex) ** 2 + (coords['y'] - ey) ** 2 if dist < 5000 and (closest is None or dist < closest[0]): closest = (dist, el) except: pass if closest: el = closest[1] selector = el.get("e2e") or el.get("cls", "").split()[0] if el.get("cls") else None tag = el.get("tag", "") text = el.get("text", "") features.append({ "type": "click", "selector": f'[data-e2e="{selector}"]' if selector else (f'{tag}:has-text("{text}")' if text else None), "description": f'点击 <{tag}> "{text[:20]}"' if text else f'点击 <{tag}>', "code": f'await page.locator("{selector or tag}").click()', "confidence": "high" if selector else "medium", "near_coords": f'({coords["x"]},{coords["y"]})', }) else: features.append({ "type": "click_coords", "selector": None, "description": f"点击坐标 ({coords['x']},{coords['y']})", "code": f'await page.mouse.click({coords["x"]}, {coords["y"]})', "confidence": "medium", }) # URL 变化特征 if analysis.get("url_changed"): url_to = analysis.get("url_to", "") if "/video/" in url_to: features.append({ "type": "navigation", "selector": 'a[href*="/video/"]', "description": "进入视频播放页", "code": 'await page.goto(video_url)', "confidence": "high", }) elif "/user/profile/" in url_to: features.append({ "type": "navigation", "selector": 'a[href*="/user/profile/"]', "description": "进入个人主页", "code": 'await page.goto(profile_url)', "confidence": "high", }) # 页面模式变化的特征 if analysis.get("mode_changed"): features.append({ "type": "page_mode", "selector": None, "description": f"页面模式: {analysis['mode_changed']}", "code": None, "confidence": "high", }) return features def _suggest_blueprint(actions: list) -> list: """从分析结果生成蓝图建议""" suggestions = [] for i, action in enumerate(actions): step_num = i + 1 at = action.get("action_type", "unknown") # 操作名 → 建议的原子操作op名 op_map = { "like": "like", "comment": "open_comment", "follow": "follow", "navigate_video": "open_video", "navigate_profile": "goto_profile", "scroll": "scroll_feed", "next_video": "next_video", "click_like_area": "like", "click_comment_area": "open_comment", "search": "search", "enter_video": "enter_video", } op = op_map.get(at, f"custom_{at}") suggestions.append({ "step": step_num, "suggested_op": op, "action_type": at, "desc": action.get("action_desc", ""), "confidence": action.get("inferred_action", {}).get("confidence", "medium"), }) return suggestions # ── 快捷入口 ── def analyze_recording_file(path: str) -> dict: """从文件路径加载并分析录制包""" from mc.recorder import RecordingSession pkg = RecordingSession.load_recording(path) return analyze_recording(pkg) if __name__ == "__main__": import sys path = sys.argv[1] if len(sys.argv) > 1 else "" if path: result = analyze_recording_file(path) print(json.dumps(result, ensure_ascii=False, indent=2)) else: print("用法: python -m mc.analyzer <录制包路径>")