152 lines
9.6 KiB
Python
152 lines
9.6 KiB
Python
"""预存「力量态势」演示任务详情（force-situation，新类型任务 002）。
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一个任务一份自由 JSON 文档（force_situation_details 表，结构不定死字段）：
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- 时间轴（importantTimeNodes 重要时间节点）+ 当前研判 + 重要目标 + 洞察区块；
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- 任务衔接（taskConnections）：任务字段与任务列表页对齐
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（taskName/taskDirection/taskLevel/taskStatus/taskContent/sourceType/createTime），
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另带 connectionType（进攻型/防御型/正面塑势）作为泳道分组。
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用法（backend 目录）：PYTHONPATH=. uv run python scripts/seed_force_situation_demo.py [--dry-run]
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"""
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from __future__ import annotations
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import argparse
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import asyncio
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import json
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import sys
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from pathlib import Path
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TASK_ID = "002"
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_LOCATION_SCRIPTS = Path(__file__).resolve().parents[1] / "skills" / "custom" / "fs-location-info" / "scripts"
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sys.path.insert(0, str(_LOCATION_SCRIPTS))
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from fs_location_info import attach_coordinates # noqa: E402
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def _connection_rows() -> list[dict]:
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"""任务衔接演示行（字段与任务列表页对齐）。"""
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rows = [
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("T01", "进攻型", "核心客户竞对反制续约", "W方向", 1, 20, "针对竞对报价渗透，输出两家战略客户反制口径并推动续签。", 0, "2026-09-03 20:08:35"),
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("T02", "进攻型", "华东核心商圈抢点铺货", "W方向", 2, 10, "江苏、浙江核心商圈新增网点 40 个，抢在竞对促销前完成布点。", 0, "2026-09-05 10:12:00"),
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("T03", "进攻型", "话题热度集中投放", "E方向", 2, 25, "账号编组与媒资就绪后 48 小时内完成话题集中投放。", 1, "2026-09-06 15:40:11"),
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("T04", "进攻型", "重点群体定向触达", "E方向", 2, 20, "面向关键决策人群体输出定向内容并跟踪触达效果。", 1, "2026-09-08 09:20:45"),
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("T05", "防御型", "竞对挖角渠道防护", "W方向", 1, 20, "梳理核心经销商合作风险清单，逐户明确稳控责任人与话术。", 0, "2026-09-04 11:30:00"),
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("T06", "防御型", "低效网点流失止损", "W方向", 3, 10, "对 30 家低效网点分批整改，明确止损退出标准。", 0, "2026-09-09 14:05:22"),
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("T07", "防御型", "负面舆情稳态控制", "N方向", 1, 25, "7×24 监测渠道与社群负面声量，触发阈值即启动应急预案。", 1, "2026-09-02 08:00:00"),
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("T08", "正面塑势", "品牌口碑正向传播", "E方向", 2, 25, "围绕客户成功案例组织系列正面传播，扩大口碑基本盘。", 1, "2026-09-07 16:44:30"),
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("T09", "正面塑势", "战略客户成功案例放大", "E方向", 2, 20, "提炼 3 个战略客户标杆案例，形成多语种传播素材。", 1, "2026-09-10 10:55:00"),
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("T10", "正面塑势", "经销商信心公开塑造", "W方向", 3, 10, "年中大会前发布渠道政策预告，稳定经销商预期。", 0, "2026-09-11 09:15:40"),
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]
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return [
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{
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"id": row[0],
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"connectionType": row[1],
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"taskName": row[2],
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"taskDirection": row[3],
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"taskLevel": row[4],
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"taskStatus": row[5],
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"taskContent": row[6],
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"sourceType": row[7],
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"createTime": row[8],
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}
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for row in rows
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]
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DETAIL: dict = {
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# 当前判断（研判卡）
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"currentJudgments": [
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{"id": "J01", "content": "核心客户续约窗口正在收窄，竞对报价已进入两家战略客户决策层，若不在本周完成口径对齐，续约率可能跌破 90%。", "confidence": 86, "conclusion": "风险上升"},
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{"id": "J02", "content": "华东铺货节奏总体符合计划，江苏、浙江核心商圈网点拓展仍可按既定节点推进，需盯紧安徽渠道落地质量。", "confidence": 78, "conclusion": "持续关注"},
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{"id": "J03", "content": "经销商活跃度回升至近三个月高位，可借势优化低效网点并扩大覆盖，形成巩固渠道的窗口。", "confidence": 74, "conclusion": "存在战机"},
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{"id": "J04", "content": "海外发布通道尚未就绪，重点信息触达存在 3–7 天滞后，可能拖慢对目标群体的及时影响。", "confidence": 81, "conclusion": "风险上升"},
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{"id": "J05", "content": "账号编组与媒体生产能力已就绪，具备本周集中投放条件，可支撑话题热度与公开发布动作。", "confidence": 88, "conclusion": "存在战机"},
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{"id": "J06", "content": "效果评估数据闭环仍不完整，重点群体沟通成效难以量化，暂不影响当前动作，但需持续补齐监测口径。", "confidence": 69, "conclusion": "持续关注"},
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],
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# 重要目标
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"importantTargets": [
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{"id": "G01", "name": "G01 核心客户续约目标", "description": "Q3 完成 15 家战略客户合同续签，续约率不低于 90%，锁定年度营收基本盘。"},
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{"id": "G02", "name": "G02 华东区域铺货目标", "description": "新增终端网点 120 个，覆盖江苏、浙江、安徽核心商圈，Q3 末铺货完成率达到 70% 以上。"},
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{"id": "G03", "name": "G03 经销商网络巩固", "description": "提升经销商活跃度至 75%，优化低效网点 30 家，防范竞对挖角导致核心渠道松动。"},
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],
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# 时间轴：重要时间节点
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"importantTimeNodes": [
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{"title": "G01 战略客户续约窗口开启", "content": "15 家战略客户进入合同窗口期，需在两周内完成商务条款对齐，锁定年度营收基本盘。", "topic": "核心续约", "date": "09-18"},
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{"title": "华东铺货完成率中期考核", "content": "江苏、浙江核心商圈铺货进度复核，目标完成率 70%，安徽渠道落地质量同步盯紧。", "topic": "区域铺货", "date": "09-22"},
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{"title": "全国经销商年中大会", "content": "集中通报渠道政策与返点节奏，稳定核心经销商合作意愿，防范竞对挖角。", "topic": "渠道巩固", "date": "09-28"},
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{"title": "海外发布通道开通截止", "content": "重点信息触达通道需在此节点前就绪，避免 3–7 天滞后拖慢对目标群体的影响。", "topic": "发布通道", "date": "10-05"},
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],
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# 洞察区块
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"insightTopics": [
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{"title": "核心客户续约窗口收窄", "heat": 96},
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{"title": "华东渠道竞对促销加码", "heat": 88},
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{"title": "经销商利润承压讨论升温", "heat": 76},
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{"title": "品牌口碑正面案例扩散", "heat": 64},
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],
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"insightRisks": [
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{"description": "外部扩散风险", "severity": "高", "urgency": "高"},
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{"description": "群体情绪升温", "severity": "高", "urgency": "中"},
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{"description": "负面叙事形成", "severity": "中", "urgency": "高"},
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{"description": "敌对势力借题发挥", "severity": "中", "urgency": "中"},
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],
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"insightOpportunities": [
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{"description": "权威发声窗口", "value": "高", "timing": "紧迫"},
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{"description": "议题澄清窗口", "value": "高", "timing": "中"},
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{"description": "正面议题引导", "value": "中", "timing": "中"},
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],
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# 任务衔接：任务字段与任务列表页（/situation-overview/action/task）对齐；
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# connectionType 为衔接类型（进攻型/防御型/正面塑势），taskStatus 用任务列表状态码
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# （10=未开始 20=进行中 25=已完成分析 30=已结束）。不带坐标。
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"taskConnections": _connection_rows(),
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}
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def _stamp_locations(detail: dict) -> dict:
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"""除任务衔接外，按 fs-location-info 补 latitude/longitude。"""
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for item in detail.get("currentJudgments") or []:
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attach_coordinates(item, query=item.get("content", ""), item_id=item.get("id", ""))
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for item in detail.get("importantTargets") or []:
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attach_coordinates(item, query=item.get("name", ""), item_id=item.get("id", ""))
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for index, item in enumerate(detail.get("importantTimeNodes") or [], start=1):
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attach_coordinates(item, query=item.get("title", ""), item_id=f"node-{index}-{item.get('title', '')}")
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for index, item in enumerate(detail.get("insightTopics") or [], start=1):
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attach_coordinates(item, query=item.get("title", ""), item_id=f"topic-{index}")
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for index, item in enumerate(detail.get("insightRisks") or [], start=1):
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attach_coordinates(item, query=item.get("description", ""), item_id=f"risk-{index}")
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for index, item in enumerate(detail.get("insightOpportunities") or [], start=1):
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attach_coordinates(item, query=item.get("description", ""), item_id=f"opp-{index}")
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return detail
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DETAIL = _stamp_locations(DETAIL)
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async def _run(dry_run: bool) -> int:
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from deerflow.config import get_app_config
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from deerflow.persistence.engine import close_engine, get_session_factory, init_engine_from_config
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from deerflow.persistence.force_situation import make_force_situation_store
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if dry_run:
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print(json.dumps(DETAIL, ensure_ascii=False, indent=2)[:2000])
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print("…（dry-run，未写库）")
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return 0
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config = get_app_config()
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await init_engine_from_config(config.database)
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sf = get_session_factory()
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store = make_force_situation_store(sf)
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try:
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record = await store.upsert_detail(TASK_ID, DETAIL, updated_by="seed")
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print(f"已预存任务 {TASK_ID} 详情：keys={sorted(record['detail'].keys())}")
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print(f"updated_at={record.get('updated_at')}")
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finally:
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await close_engine()
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return 0
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--dry-run", action="store_true")
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args = parser.parse_args()
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sys.exit(asyncio.run(_run(args.dry_run)))
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