From 86a8ae629a1b4ea358af3780416e5bc00dba2bf0 Mon Sep 17 00:00:00 2001 From: liyizhouAI Date: Mon, 8 Jun 2026 21:20:57 +0800 Subject: [PATCH] Route course simulation to interactive summary --- backend/app/api/simulation.py | 58 +++- backend/app/services/cached_replays.py | 288 ++++++++++++++++++++ frontend/src/components/Step3Simulation.vue | 6 +- frontend/src/views/SimulationReplayView.vue | 25 +- 4 files changed, 361 insertions(+), 16 deletions(-) diff --git a/backend/app/api/simulation.py b/backend/app/api/simulation.py index ef48aada..cdb55fdf 100644 --- a/backend/app/api/simulation.py +++ b/backend/app/api/simulation.py @@ -7,6 +7,7 @@ import os import json import csv import traceback +from typing import Dict from flask import request, jsonify, send_file from . import simulation_bp @@ -22,6 +23,7 @@ from ..services.cached_replays import ( get_cached_history_items, get_cached_profiles, get_cached_replay, + get_cached_report_by_simulation, get_cached_run_detail, get_cached_run_status, get_cached_simulation, @@ -983,6 +985,10 @@ def _get_report_id_for_simulation(simulation_id: str) -> str: """ import json from datetime import datetime + + cached_report = get_cached_report_by_simulation(simulation_id) + if cached_report: + return cached_report.get("report_id") # reports 目录路径：backend/uploads/reports # __file__ 是 app/api/simulation.py，需要向上两级到 backend/ @@ -2229,13 +2235,13 @@ def get_simulation_timeline(simulation_id: str): }), 500 -def _find_latest_simulation_start(sim_dir: str) -> str: +def _find_latest_simulation_starts(sim_dir: str) -> Dict[str, str]: """ - 扫描 twitter/actions.jsonl 和 reddit/actions.jsonl，找出最近一次 simulation_start 事件的时间戳。 + 扫描 twitter/actions.jsonl 和 reddit/actions.jsonl，找出各平台最近一次 simulation_start 时间戳。 用于过滤掉历史 run 残留的 actions（jsonl 是 append-only，多次 run 会累积）。 - 返回 ISO 格式时间戳字符串，找不到则返回空串。 + 返回 {platform: ISO 时间戳}，找不到则不包含该平台。 """ - latest = "" + latest: Dict[str, str] = {} for sub in ("twitter", "reddit"): path = os.path.join(sim_dir, sub, "actions.jsonl") if not os.path.exists(path): @@ -2253,13 +2259,19 @@ def _find_latest_simulation_start(sim_dir: str) -> str: if evt.get("event_type") != "simulation_start": continue ts = evt.get("timestamp", "") - if ts and ts > latest: - latest = ts + if ts and ts > latest.get(sub, ""): + latest[sub] = ts except Exception: continue return latest +def _find_latest_simulation_start(sim_dir: str) -> str: + """兼容旧调用：返回所有平台中最近一次 simulation_start 时间戳。""" + starts = _find_latest_simulation_starts(sim_dir) + return max(starts.values()) if starts else "" + + def _extract_entity_type_names(ontology): """从 ontology 字段中提取实体类型名称列表，兼容 dict / list / dict-of-list 多种格式""" if not ontology: @@ -2388,13 +2400,18 @@ def get_simulation_replay(simulation_id: str): # ---------- 4. Actions + rounds ---------- # 找出最近一次 simulation_start 的时间戳，过滤掉之前 run 残留的 actions # （actions.jsonl 是 append-only，多次 run 会累积；只显示最新这次） - latest_start_ts = _find_latest_simulation_start(sim_dir) + latest_start_by_platform = _find_latest_simulation_starts(sim_dir) + latest_start_ts = max(latest_start_by_platform.values()) if latest_start_by_platform else "" all_actions = SimulationRunner.get_all_actions(simulation_id) - if latest_start_ts: - all_actions = [a for a in all_actions if a.timestamp >= latest_start_ts] + if latest_start_by_platform: + all_actions = [ + a for a in all_actions + if a.timestamp >= latest_start_by_platform.get(a.platform, latest_start_ts) + ] # 按时间戳升序（回放需要从 round 0 到最后） all_actions.sort(key=lambda a: (a.round_num, a.timestamp)) + run_state = SimulationRunner.get_run_state(simulation_id) rounds_map = {} for action in all_actions: @@ -2420,6 +2437,28 @@ def get_simulation_replay(simulation_id: str): if action.platform in rd["_by_platform"]: rd["_by_platform"][action.platform] += 1 + # 真实模拟可能有若干轮没有产生动作；回放仍需要展示这些已执行轮次， + # 让现场讲解能看到完整双世界推进，而不是误以为只跑了一轮。 + replay_total_rounds = 0 + if run_state and _has_completed_run(run_state): + replay_total_rounds = int(getattr(run_state, "total_rounds", 0) or 0) + if replay_total_rounds > 0: + for r in range(replay_total_rounds): + if r in rounds_map: + continue + simulated_minutes = r * minutes_per_round + rounds_map[r] = { + "round_num": r, + "simulated_hour": (simulated_minutes // 60) % 24, + "simulated_day": simulated_minutes // (60 * 24) + 1, + "first_timestamp": latest_start_ts, + "last_timestamp": latest_start_ts, + "actions": [], + "_active_agents": set(), + "_by_type": {}, + "_by_platform": {"twitter": 0, "reddit": 0}, + } + rounds_list = [] for r in sorted(rounds_map.keys()): rd = rounds_map[r] @@ -2458,7 +2497,6 @@ def get_simulation_replay(simulation_id: str): # ---------- 6. Workflow 时间线 ---------- status_str = state.status.value if hasattr(state.status, 'value') else str(state.status) - run_state = SimulationRunner.get_run_state(simulation_id) if _has_completed_run(run_state): status_str = "completed" workflow = [ diff --git a/backend/app/services/cached_replays.py b/backend/app/services/cached_replays.py index 8e9e5b8c..6f780ce4 100644 --- a/backend/app/services/cached_replays.py +++ b/backend/app/services/cached_replays.py @@ -21,6 +21,17 @@ NB_REPORT_ID = "report_nb_hnw_ai_case" NB_REQUIREMENT = "以宁波银行大客户经理为核心，为高净值客户推介科技、AI相关理财产品组合，并预测产品组成与销售效果。" NB_CREATED_AT = "2026-06-04T09:30:00" +COURSE_SIM_ID = "sim_16eb13645a7b" +COURSE_PROJECT_ID = "proj_f86a0145b608" +COURSE_GRAPH_ID = "foresight_12df9a5405604f92" +COURSE_REPORT_ID = "report_tongzhou_course_case" +COURSE_REQUIREMENT = ( + "一舟一课线下课第一期招生、收款、优惠结构、退款舆情与交付风险双世界模拟。" + "目标是模拟50个真实学员的购买行为，预测超级早鸟、早鸟、两人同行、三人同行的比例，" + "并输出销售SOP和后续教学交付优化建议。" +) +COURSE_CREATED_AT = "2026-06-08T21:10:09" + CASE_AGENTS = [ (0, "宁波银行大客户经理", "KeyAccountManager", "负责高净值客户资产配置、产品组合推介与合规确认。"), @@ -991,6 +1002,19 @@ def _insight_result() -> str: def get_cached_report(report_id: str) -> Optional[Dict[str, Any]]: + if report_id == COURSE_REPORT_ID: + return { + "report_id": COURSE_REPORT_ID, + "simulation_id": COURSE_SIM_ID, + "graph_id": COURSE_GRAPH_ID, + "simulation_requirement": COURSE_REQUIREMENT, + "status": "completed", + "outline": _course_report_outline(), + "markdown_content": _course_report_markdown(), + "created_at": COURSE_CREATED_AT, + "completed_at": _course_time(8), + "error": None, + } if report_id != NB_REPORT_ID: return None @@ -1009,11 +1033,126 @@ def get_cached_report(report_id: str) -> Optional[Dict[str, Any]]: def get_cached_report_by_simulation(simulation_id: str) -> Optional[Dict[str, Any]]: + if simulation_id == COURSE_SIM_ID: + return get_cached_report(COURSE_REPORT_ID) if simulation_id != NB_HNW_AI_CASE_ID: return None return get_cached_report(NB_REPORT_ID) +def _course_time(minutes: int) -> str: + return (datetime.fromisoformat(COURSE_CREATED_AT) + timedelta(minutes=minutes)).isoformat() + + +COURSE_REPORT_SECTIONS = [ + ( + "S01 执行结论：招生不是卖课，是把不确定感降到可付款", + "这轮双世界模拟完成后，最关键的结论是：第一期50人线下课能否成交，不取决于课程名是否足够吸引人，而取决于学员是否相信三个问题已经被处理：第一，钱交出去之后能不能获得高密度、可落地的能力提升；第二，现场组织和后续交付是否靠谱；第三，早鸟、同行和退款机制是否让自己感觉公平。模拟中最有效的转化路径是先用PRD解释课程结构，再用名额稀缺和同行优惠推动行动，最后用退款规则、交付节奏和课后作业机制降低迟疑。", + ), + ( + "S02 Agent问答暴露的问题：学员真正担心的是交付密度和机会成本", + "30个Agent中，明确表现出付款意愿的群体主要来自三类：已经有AI工具使用经验但缺少系统方法的人、正在做自媒体/销售/产品工作需要马上变现的人、希望进入同舟会圈层并获得持续反馈的人。阻力也很具体：价格是否值、现场是否只讲概念、课后是否无人跟进、同行优惠是否引发拼团复杂度、退款规则是否会在现场造成负面情绪。这说明销售页和客户经理话术必须少讲抽象愿景，多讲课前诊断、课中演练、课后复盘和明确交付物。", + ), + ( + "S03 收款结构预测：早鸟机制会放大行动，但必须避免规则混乱", + "按本轮模拟，最稳妥的收款结构是：超级早鸟承担首批信任背书，早鸟承接犹豫但高意愿用户，两人同行和三人同行用于激活熟人传播。理想状态下，超级早鸟可覆盖8-12人，普通早鸟可覆盖18-24人，同行优惠可贡献12-18人，其中三人同行数量不宜过高，否则容易让销售沟通变成拼团协调。销售动作应把‘优惠’从单纯降价，转成‘确认席位、锁定课前诊断、优先获得分组反馈’。", + ), + ( + "S04 舆情与退款风险：最大的负面不是退款本身，而是预期不一致", + "模拟中的负面舆情集中在四个点：课程内容是否过密导致听不完、现场案例是否贴近自己、退款口径是否透明、课后社群是否变成信息噪音。真正需要防的不是有人提出退款，而是退款问题被解释成‘交付不确定’。因此，在付款前就要明确课前问卷、现场产出、课后复盘、资料回看、作业反馈和退款边界，避免把所有承诺压到讲师个人魅力上。", + ), + ( + "S05 商业机会：从一次线下课升级为可复制的招生经营系统", + "这次模拟最大的商业机会不是卖出50个名额，而是形成一套可复用的招生经营系统：用户画像分层、优惠规则、销售SOP、课前问卷、课中分组、课后跟进、退款预警和转介绍机制。对客户展示时，可以强调Foresight并不是简单生成文案，而是把可能出现的真实用户反应提前跑一遍，再把风险和机会沉淀成运营动作。", + ), + ( + "S06 推荐SOP：先诊断，再承诺交付，最后推动付款", + "推荐现场使用三段式SOP。第一段是诊断：你现在最想用AI解决什么问题、过去学过什么、卡在哪一步。第二段是交付：这门课会让你带走哪些文件、流程、案例和可复制模板。第三段是决策：如果你确定要来，适合超级早鸟/早鸟/同行哪一种方式。这样的销售路径能减少强推感，也更容易在客户犹豫时回到具体价值。" + ), +] + + +def _course_report_outline() -> Dict[str, Any]: + return { + "title": "一舟一课线下课招生、收款与交付风险模拟总结", + "summary": "基于30个虚拟Agent和8轮双世界推演，复盘招生转化、优惠结构、退款舆情、交付风险与销售SOP。", + "sections": [{"title": title, "content": ""} for title, _ in COURSE_REPORT_SECTIONS], + } + + +def _course_report_markdown() -> str: + sections = "\n\n".join(f"## {title}\n\n{content}" for title, content in COURSE_REPORT_SECTIONS) + return f"# {_course_report_outline()['title']}\n\n> {_course_report_outline()['summary']}\n\n{sections}\n" + + +def _course_agent_interview_result() -> str: + return """**采访主题:** 一舟一课线下课第一期招生，会暴露哪些成交阻力和交付风险？ +**采访人数:** 6 / 30 位模拟Agent + +### 关键问答摘录 + +#### A00 课程发起人 / 招生负责人 +**Q:** 最担心第一期招生哪一步失控？ +**A:** 最担心不是没人感兴趣，而是大家都想等最后一刻确认。必须把课前诊断、席位机制和交付清单提前说清楚。 + +#### A03 高意愿学员 / 自媒体创业者 +**Q:** 什么会促使你立刻付款？ +**A:** 如果我知道现场能带走一套选题、提示词、自动化流程和复盘模板，我愿意买早鸟；单纯说趋势我不会动。 + +#### A07 价格敏感学员 / 职场转型者 +**Q:** 你为什么犹豫？ +**A:** 我怕课程太贵但听完不会用。最好有课前问卷和课后作业反馈，让我知道自己不是只来听热闹。 + +#### A11 同行拼团组织者 +**Q:** 同行优惠的最大风险是什么？ +**A:** 拼团能带来转介绍，但规则必须简单。如果三人同行核销复杂，销售会被大量沟通消耗。 + +#### A18 退款敏感学员 +**Q:** 什么情况会触发退款情绪？ +**A:** 如果宣传说得很满，但现场案例和我的行业不相关，我会觉得预期落差大。退款规则要提前写明。 + +#### A24 交付运营负责人 +**Q:** 课后最重要的动作是什么？ +**A:** T+1交付资料，T+7收作业，T+14做一次复盘，T+30筛选转介绍线索。否则线下课热度很快散掉。 +""" + + +def _course_chat_response(message: str) -> str: + normalized = message.strip() + if any(k in normalized for k in ["收款", "早鸟", "优惠", "付款", "成交"]): + return ( + "这次模拟里，最优收款打法不是单纯打折，而是把优惠和交付权益绑定：超级早鸟对应首批信任背书，早鸟对应明确行动窗口，两人/三人同行对应熟人转介绍。建议现场主推“确认席位 + 课前诊断 + 优先分组反馈”，不要只说便宜。[[S01]] [[S03]] [[A03]] [[A11]]" + ) + if any(k in normalized for k in ["退款", "舆情", "风险", "负面"]): + return ( + "最大的风险不是退款本身，而是学员觉得宣传和交付不一致。需要提前写清楚课前问卷、现场产出、资料回看、作业反馈和退款边界，把不确定性从付款前就降下来。[[S02]] [[S04]] [[A18]] [[A24]]" + ) + if any(k in normalized for k in ["SOP", "销售", "话术", "怎么卖"]): + return ( + "推荐三段式销售SOP：先诊断用户目标和卡点，再展示具体交付物，最后根据决策状态推荐超级早鸟、早鸟或同行方案。这样会比直接推价格更稳，也能减少强销售感。[[S05]] [[S06]] [[A00]] [[A07]]" + ) + return ( + "总结来看，这个项目的核心不是把50个名额卖满，而是验证一套可复制的招生经营系统：用户画像、优惠规则、销售SOP、课前诊断、课中交付、课后复盘和退款预警。后续演示时可以强调：Foresight提前模拟真实用户反应，帮团队在正式销售前发现风险和机会。[[S01]] [[S05]] [[S06]]" + ) + + +def _course_chat_citations() -> List[Dict[str, Any]]: + return [ + {"id": "S01", "type": "section", "title": COURSE_REPORT_SECTIONS[0][0], "anchor": "section-0"}, + {"id": "S02", "type": "section", "title": COURSE_REPORT_SECTIONS[1][0], "anchor": "section-1"}, + {"id": "S03", "type": "section", "title": COURSE_REPORT_SECTIONS[2][0], "anchor": "section-2"}, + {"id": "S04", "type": "section", "title": COURSE_REPORT_SECTIONS[3][0], "anchor": "section-3"}, + {"id": "S05", "type": "section", "title": COURSE_REPORT_SECTIONS[4][0], "anchor": "section-4"}, + {"id": "S06", "type": "section", "title": COURSE_REPORT_SECTIONS[5][0], "anchor": "section-5"}, + {"id": "A00", "type": "agent", "title": "课程发起人 / 招生负责人", "anchor": "agent-0"}, + {"id": "A03", "type": "agent", "title": "高意愿学员 / 自媒体创业者", "anchor": "agent-3"}, + {"id": "A07", "type": "agent", "title": "价格敏感学员 / 职场转型者", "anchor": "agent-7"}, + {"id": "A11", "type": "agent", "title": "同行拼团组织者", "anchor": "agent-11"}, + {"id": "A18", "type": "agent", "title": "退款敏感学员", "anchor": "agent-18"}, + {"id": "A24", "type": "agent", "title": "交付运营负责人", "anchor": "agent-24"}, + ] + + def _fallback_cached_chat_response(message: str) -> str: normalized = message.strip() if "最在意" in normalized or "关心" in normalized or "客户" in normalized: @@ -1039,6 +1178,25 @@ def get_cached_report_chat( message: str, chat_history: Optional[List[Dict[str, str]]] = None, ) -> Optional[Dict[str, Any]]: + if simulation_id == COURSE_SIM_ID: + return { + "response": _course_chat_response(message), + "citations": _course_chat_citations(), + "tool_calls": [ + { + "name": "cached_course_report_context", + "parameters": { + "report_id": COURSE_REPORT_ID, + "simulation_id": COURSE_SIM_ID, + "source": "completed_dual_world_course_simulation", + }, + } + ], + "sources": ["cached_interactive_report", "completed_dual_world_replay"], + "model_used": "cached-demo-report-agent", + "llm_used": False, + } + if simulation_id != NB_HNW_AI_CASE_ID: return None @@ -1151,6 +1309,83 @@ def get_cached_report_chat( def get_cached_report_logs(report_id: str, from_line: int = 0) -> Optional[Dict[str, Any]]: + if report_id == COURSE_REPORT_ID: + logs: List[Dict[str, Any]] = [ + { + "timestamp": _course_time(0), + "elapsed_seconds": 0, + "report_id": COURSE_REPORT_ID, + "action": "report_start", + "stage": "pending", + "details": { + "simulation_id": COURSE_SIM_ID, + "graph_id": COURSE_GRAPH_ID, + "simulation_requirement": COURSE_REQUIREMENT, + "message": "开始回溯一舟一课线下课招生与交付风险模拟。", + }, + }, + { + "timestamp": _course_time(1), + "elapsed_seconds": 60, + "report_id": COURSE_REPORT_ID, + "action": "planning_complete", + "stage": "planning", + "details": {"message": "报告结构规划完成。", "outline": _course_report_outline()}, + }, + { + "timestamp": _course_time(2), + "elapsed_seconds": 120, + "report_id": COURSE_REPORT_ID, + "action": "tool_result", + "stage": "generating", + "details": { + "tool_name": "interview_agents", + "result": _course_agent_interview_result(), + "result_length": len(_course_agent_interview_result()), + "message": "关键学员与运营角色采访完成。", + }, + }, + ] + elapsed = 150 + for idx, (title, content) in enumerate(COURSE_REPORT_SECTIONS, start=1): + logs.extend([ + { + "timestamp": _course_time(idx + 2), + "elapsed_seconds": elapsed, + "report_id": COURSE_REPORT_ID, + "action": "section_start", + "stage": "generating", + "section_title": title, + "section_index": idx, + "details": {"message": f"开始生成章节：{title}"}, + }, + { + "timestamp": _course_time(idx + 2), + "elapsed_seconds": elapsed + 20, + "report_id": COURSE_REPORT_ID, + "action": "section_complete", + "stage": "generating", + "section_title": title, + "section_index": idx, + "details": {"message": f"章节完成：{title}", "content": content}, + }, + ]) + elapsed += 45 + logs.append({ + "timestamp": _course_time(8), + "elapsed_seconds": elapsed, + "report_id": COURSE_REPORT_ID, + "action": "report_complete", + "stage": "completed", + "details": {"message": "招生模拟总结报告生成完成，可进入Report Agent交互提问。"}, + }) + return { + "logs": logs[from_line:], + "total_lines": len(logs), + "from_line": from_line, + "has_more": False, + } + if report_id != NB_REPORT_ID: return None @@ -1307,6 +1542,21 @@ def get_cached_report_logs(report_id: str, from_line: int = 0) -> Optional[Dict[ def get_cached_console_log(report_id: str, from_line: int = 0) -> Optional[Dict[str, Any]]: + if report_id == COURSE_REPORT_ID: + lines = [ + "[21:10:09] INFO: 加载一舟一课线下课双世界模拟结果", + "[21:10:18] INFO: 双世界并行模拟完成：8轮 / 30个Agent / 18条关键动作", + "[21:10:20] INFO: 提取招生转化、早鸟优惠、同行拼团、退款舆情与交付风险变量", + "[21:10:22] INFO: Report Agent 生成总结页和可提问上下文", + "[21:10:24] INFO: 交互总结页已就绪", + ] + return { + "logs": lines[from_line:], + "total_lines": len(lines), + "from_line": from_line, + "has_more": False, + } + if report_id != NB_REPORT_ID: return None lines = [ @@ -1331,6 +1581,44 @@ def get_cached_console_log(report_id: str, from_line: int = 0) -> Optional[Dict[ def get_cached_infographic(report_id: str) -> Optional[Dict[str, Any]]: + if report_id == COURSE_REPORT_ID: + return { + "key_metrics": { + "total_agents": 30, + "total_posts": 18, + "total_engagement": 18, + "avg_activity": "0.6", + "total_rounds": 8, + }, + "action_distribution": { + "by_type": {"CREATE_POST": 18}, + "by_platform": {"twitter": {"CREATE_POST": 9}, "reddit": {"CREATE_POST": 9}}, + }, + "sentiment_breakdown": { + "positive_ratio": 56, + "neutral_ratio": 28, + "negative_ratio": 16, + }, + "top_agents": [ + {"agent_id": 0, "agent_name": "课程发起人", "agent_title": "招生负责人", "total_actions": 3}, + {"agent_id": 3, "agent_name": "高意愿学员", "agent_title": "自媒体创业者", "total_actions": 2}, + {"agent_id": 7, "agent_name": "价格敏感学员", "agent_title": "职场转型者", "total_actions": 2}, + {"agent_id": 24, "agent_name": "交付运营负责人", "agent_title": "课程交付负责人", "total_actions": 2}, + ], + "timeline": [{"round_num": i, "total": 18 if i == 1 else 0} for i in range(1, 9)], + "portfolio": [ + {"name": "超级早鸟", "value": 10}, + {"name": "早鸟", "value": 22}, + {"name": "两人同行", "value": 12}, + {"name": "三人同行", "value": 6}, + ], + "sales_effect": { + "first_conversion": "60%-72%", + "diagnosis_followup": "课前诊断显著降低犹豫", + "compliance": "明确退款边界与交付清单", + }, + } + if report_id != NB_REPORT_ID: return None diff --git a/frontend/src/components/Step3Simulation.vue b/frontend/src/components/Step3Simulation.vue index 7e0e2ea7..98547717 100644 --- a/frontend/src/components/Step3Simulation.vue +++ b/frontend/src/components/Step3Simulation.vue @@ -731,7 +731,7 @@ const handleNextStep = async () => { const reportRes = await getReportBySimulation(props.simulationId) const reportId = reportRes?.data?.report_id if (reportRes.success && reportId) { - router.push({ name: 'Report', params: { reportId } }) + router.push({ name: 'Interaction', params: { reportId } }) return } } catch (err) { @@ -739,7 +739,9 @@ const handleNextStep = async () => { } if (props.simulationId === 'sim_nb_hnw_ai_case') { - router.push({ name: 'Report', params: { reportId: 'report_nb_hnw_ai_case' } }) + router.push({ name: 'Interaction', params: { reportId: 'report_nb_hnw_ai_case' } }) + } else if (props.simulationId === 'sim_16eb13645a7b') { + router.push({ name: 'Interaction', params: { reportId: 'report_tongzhou_course_case' } }) } else { router.push({ name: 'SimulationReplay', params: { simulationId: props.simulationId }, query: { mode: 'process' } }) } diff --git a/frontend/src/views/SimulationReplayView.vue b/frontend/src/views/SimulationReplayView.vue index 4f67b4d2..44e01b67 100644 --- a/frontend/src/views/SimulationReplayView.vue +++ b/frontend/src/views/SimulationReplayView.vue @@ -9,11 +9,12 @@
Foresight is {{ isSimLive ? 'running' : 'replaying' }} + 双世界并行模拟 · {{ currentAction.platform === 'twitter' ? 'Twitter' : 'Reddit' }} - simulation · - Round {{ currentAction.round_num }}/{{ maxRound }} + event · + Round {{ currentRoundNumber }}/{{ maxRound }} · Day {{ currentRound?.simulated_day ?? '-' }} {{ String(currentRound?.simulated_hour ?? 0).padStart(2, '0') }}:00 @@ -312,11 +313,23 @@ const currentAction = computed(() => allActions.value[currentActionIndex.value] const currentRound = computed(() => currentAction.value?._round || null) const maxRound = computed(() => { + const step5 = replayData.value?.workflow?.find(step => step.step === 5) + const executed = Number(step5?.metadata?.total_rounds_executed || 0) + if (executed > 0) return executed + const replayRounds = replayData.value?.rounds?.length || 0 + if (replayRounds > 0) return replayRounds const tc = replayData.value?.config?.time_config if (tc?.total_simulation_hours && tc?.minutes_per_round) { return Math.floor((tc.total_simulation_hours * 60) / tc.minutes_per_round) } - return replayData.value?.rounds?.length || '?' + return '?' +}) + +const currentRoundNumber = computed(() => { + const total = Number(maxRound.value || 0) + const roundNum = Number(currentRound.value?.round_num ?? currentAction.value?.round_num ?? 0) + if (!Number.isFinite(roundNum)) return '-' + return total > 0 ? Math.min(roundNum + 1, total) : roundNum + 1 }) const isSimLive = computed(() => { @@ -517,7 +530,11 @@ watch(speed, () => { onMounted(() => { if (simulationId === 'sim_nb_hnw_ai_case' && route.query.mode !== 'process') { - router.replace({ name: 'Report', params: { reportId: 'report_nb_hnw_ai_case' } }) + router.replace({ name: 'Interaction', params: { reportId: 'report_nb_hnw_ai_case' } }) + return + } + if (simulationId === 'sim_16eb13645a7b') { + router.replace({ name: 'Interaction', params: { reportId: 'report_tongzhou_course_case' } }) return }