Route course simulation to interactive summary
This commit is contained in:
liyizhouAI
parent
57263d5337
commit
86a8ae629a
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@ -7,6 +7,7 @@ import os
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import json
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import csv
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import traceback
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from typing import Dict
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from flask import request, jsonify, send_file
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from . import simulation_bp
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@ -22,6 +23,7 @@ from ..services.cached_replays import (
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get_cached_history_items,
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get_cached_profiles,
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get_cached_replay,
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get_cached_report_by_simulation,
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get_cached_run_detail,
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get_cached_run_status,
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get_cached_simulation,
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@ -983,6 +985,10 @@ def _get_report_id_for_simulation(simulation_id: str) -> str:
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"""
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import json
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from datetime import datetime
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cached_report = get_cached_report_by_simulation(simulation_id)
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if cached_report:
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return cached_report.get("report_id")
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# reports 目录路径：backend/uploads/reports
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# __file__ 是 app/api/simulation.py，需要向上两级到 backend/
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@ -2229,13 +2235,13 @@ def get_simulation_timeline(simulation_id: str):
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}), 500
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def _find_latest_simulation_start(sim_dir: str) -> str:
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def _find_latest_simulation_starts(sim_dir: str) -> Dict[str, str]:
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"""
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扫描 twitter/actions.jsonl 和 reddit/actions.jsonl，找出最近一次 simulation_start 事件的时间戳。
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扫描 twitter/actions.jsonl 和 reddit/actions.jsonl，找出各平台最近一次 simulation_start 时间戳。
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用于过滤掉历史 run 残留的 actions（jsonl 是 append-only，多次 run 会累积）。
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返回 ISO 格式时间戳字符串，找不到则返回空串。
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返回 {platform: ISO 时间戳}，找不到则不包含该平台。
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"""
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latest = ""
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latest: Dict[str, str] = {}
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for sub in ("twitter", "reddit"):
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path = os.path.join(sim_dir, sub, "actions.jsonl")
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if not os.path.exists(path):
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@ -2253,13 +2259,19 @@ def _find_latest_simulation_start(sim_dir: str) -> str:
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if evt.get("event_type") != "simulation_start":
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continue
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ts = evt.get("timestamp", "")
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if ts and ts > latest:
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latest = ts
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if ts and ts > latest.get(sub, ""):
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latest[sub] = ts
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except Exception:
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continue
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return latest
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def _find_latest_simulation_start(sim_dir: str) -> str:
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"""兼容旧调用：返回所有平台中最近一次 simulation_start 时间戳。"""
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starts = _find_latest_simulation_starts(sim_dir)
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return max(starts.values()) if starts else ""
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def _extract_entity_type_names(ontology):
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"""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"""
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if not ontology:
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@ -2388,13 +2400,18 @@ def get_simulation_replay(simulation_id: str):
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# ---------- 4. Actions + rounds ----------
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# 找出最近一次 simulation_start 的时间戳，过滤掉之前 run 残留的 actions
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# （actions.jsonl 是 append-only，多次 run 会累积；只显示最新这次）
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latest_start_ts = _find_latest_simulation_start(sim_dir)
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latest_start_by_platform = _find_latest_simulation_starts(sim_dir)
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latest_start_ts = max(latest_start_by_platform.values()) if latest_start_by_platform else ""
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all_actions = SimulationRunner.get_all_actions(simulation_id)
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if latest_start_ts:
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all_actions = [a for a in all_actions if a.timestamp >= latest_start_ts]
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if latest_start_by_platform:
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all_actions = [
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a for a in all_actions
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if a.timestamp >= latest_start_by_platform.get(a.platform, latest_start_ts)
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]
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# 按时间戳升序（回放需要从 round 0 到最后）
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all_actions.sort(key=lambda a: (a.round_num, a.timestamp))
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run_state = SimulationRunner.get_run_state(simulation_id)
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rounds_map = {}
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for action in all_actions:
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@ -2420,6 +2437,28 @@ def get_simulation_replay(simulation_id: str):
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if action.platform in rd["_by_platform"]:
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rd["_by_platform"][action.platform] += 1
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# 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，
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# 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。
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replay_total_rounds = 0
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if run_state and _has_completed_run(run_state):
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replay_total_rounds = int(getattr(run_state, "total_rounds", 0) or 0)
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if replay_total_rounds > 0:
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for r in range(replay_total_rounds):
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if r in rounds_map:
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continue
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simulated_minutes = r * minutes_per_round
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rounds_map[r] = {
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"round_num": r,
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"simulated_hour": (simulated_minutes // 60) % 24,
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"simulated_day": simulated_minutes // (60 * 24) + 1,
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"first_timestamp": latest_start_ts,
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"last_timestamp": latest_start_ts,
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"actions": [],
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"_active_agents": set(),
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"_by_type": {},
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"_by_platform": {"twitter": 0, "reddit": 0},
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}
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rounds_list = []
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for r in sorted(rounds_map.keys()):
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rd = rounds_map[r]
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@ -2458,7 +2497,6 @@ def get_simulation_replay(simulation_id: str):
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# ---------- 6. Workflow 时间线 ----------
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status_str = state.status.value if hasattr(state.status, 'value') else str(state.status)
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run_state = SimulationRunner.get_run_state(simulation_id)
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if _has_completed_run(run_state):
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status_str = "completed"
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workflow = [
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@ -21,6 +21,17 @@ NB_REPORT_ID = "report_nb_hnw_ai_case"
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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。"
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NB_CREATED_AT = "2026-06-04T09:30:00"
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COURSE_SIM_ID = "sim_16eb13645a7b"
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COURSE_PROJECT_ID = "proj_f86a0145b608"
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COURSE_GRAPH_ID = "foresight_12df9a5405604f92"
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COURSE_REPORT_ID = "report_tongzhou_course_case"
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COURSE_REQUIREMENT = (
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"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。"
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"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，"
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"并输出销售SOP和后续教学交付优化建议。"
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)
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COURSE_CREATED_AT = "2026-06-08T21:10:09"
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CASE_AGENTS = [
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(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。"),
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@ -991,6 +1002,19 @@ def _insight_result() -> str:
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def get_cached_report(report_id: str) -> Optional[Dict[str, Any]]:
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if report_id == COURSE_REPORT_ID:
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return {
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"report_id": COURSE_REPORT_ID,
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"simulation_id": COURSE_SIM_ID,
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"graph_id": COURSE_GRAPH_ID,
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"simulation_requirement": COURSE_REQUIREMENT,
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"status": "completed",
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"outline": _course_report_outline(),
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"markdown_content": _course_report_markdown(),
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"created_at": COURSE_CREATED_AT,
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"completed_at": _course_time(8),
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"error": None,
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}
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if report_id != NB_REPORT_ID:
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return None
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@ -1009,11 +1033,126 @@ def get_cached_report(report_id: str) -> Optional[Dict[str, Any]]:
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def get_cached_report_by_simulation(simulation_id: str) -> Optional[Dict[str, Any]]:
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if simulation_id == COURSE_SIM_ID:
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return get_cached_report(COURSE_REPORT_ID)
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if simulation_id != NB_HNW_AI_CASE_ID:
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return None
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return get_cached_report(NB_REPORT_ID)
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def _course_time(minutes: int) -> str:
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return (datetime.fromisoformat(COURSE_CREATED_AT) + timedelta(minutes=minutes)).isoformat()
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COURSE_REPORT_SECTIONS = [
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(
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"S01 执行结论：招生不是卖课，是把不确定感降到可付款",
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"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。",
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),
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(
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"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",
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"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。",
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),
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(
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"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",
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"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’。",
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),
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(
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"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",
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"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。",
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),
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(
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"S05 商业机会：从一次线下课升级为可复制的招生经营系统",
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"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。",
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),
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(
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"S06 推荐SOP：先诊断，再承诺交付，最后推动付款",
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"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。"
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),
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]
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def _course_report_outline() -> Dict[str, Any]:
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return {
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"title": "一舟一课线下课招生、收款与交付风险模拟总结",
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"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。",
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"sections": [{"title": title, "content": ""} for title, _ in COURSE_REPORT_SECTIONS],
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}
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def _course_report_markdown() -> str:
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sections = "\n\n".join(f"## {title}\n\n{content}" for title, content in COURSE_REPORT_SECTIONS)
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return f"# {_course_report_outline()['title']}\n\n> {_course_report_outline()['summary']}\n\n{sections}\n"
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def _course_agent_interview_result() -> str:
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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？
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**采访人数:** 6 / 30 位模拟Agent
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### 关键问答摘录
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#### A00 课程发起人 / 招生负责人
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**Q:** 最担心第一期招生哪一步失控？
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**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。
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#### A03 高意愿学员 / 自媒体创业者
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**Q:** 什么会促使你立刻付款？
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**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。
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#### A07 价格敏感学员 / 职场转型者
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**Q:** 你为什么犹豫？
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**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。
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#### A11 同行拼团组织者
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**Q:** 同行优惠的最大风险是什么？
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**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。
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#### A18 退款敏感学员
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**Q:** 什么情况会触发退款情绪？
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**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。
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#### A24 交付运营负责人
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**Q:** 课后最重要的动作是什么？
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**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。
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"""
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def _course_chat_response(message: str) -> str:
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normalized = message.strip()
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if any(k in normalized for k in ["收款", "早鸟", "优惠", "付款", "成交"]):
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return (
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"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]]"
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)
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if any(k in normalized for k in ["退款", "舆情", "风险", "负面"]):
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return (
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"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]]"
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)
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if any(k in normalized for k in ["SOP", "销售", "话术", "怎么卖"]):
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return (
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"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]]"
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)
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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
|
||||
|
||||
|
|
|
|||
|
|
@ -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' } })
|
||||
}
|
||||
|
|
|
|||
|
|
@ -9,11 +9,12 @@
|
|||
<div class="title-sub">
|
||||
<span v-if="currentAction">
|
||||
Foresight is {{ isSimLive ? 'running' : 'replaying' }}
|
||||
双世界并行模拟 ·
|
||||
<span class="platform-tag" :class="currentAction.platform">
|
||||
{{ currentAction.platform === 'twitter' ? 'Twitter' : 'Reddit' }}
|
||||
</span>
|
||||
simulation ·
|
||||
<span class="sub-detail">Round {{ currentAction.round_num }}/{{ maxRound }}</span>
|
||||
event ·
|
||||
<span class="sub-detail">Round {{ currentRoundNumber }}/{{ maxRound }}</span>
|
||||
·
|
||||
<span class="sub-detail">Day {{ currentRound?.simulated_day ?? '-' }}
|
||||
{{ String(currentRound?.simulated_hour ?? 0).padStart(2, '0') }}:00</span>
|
||||
|
|
@ -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
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue