Evilom
fe5505343e
- 通过悟空(dws CLI)拉取dc战略问题研究院+创新组两个群的消息(377条) - 提取190个飞书链接、18个文件附件 - 下载HTML/MD/XLSX等报告文件到output/downloaded-files/ - 构建知识图谱(JSON+HTML可视化) - 生成Obsidian知识库(28个页面,7大主题) - 生成花园世界全量汇总报告 - 所有脚本路径改为相对路径,便于迁移
283 lines
10 KiB
Python
283 lines
10 KiB
Python
import sys, io, json, re
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sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8")
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import os
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SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
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PROJECT_ROOT = os.path.dirname(SCRIPT_DIR)
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with open(os.path.join(PROJECT_ROOT, "data", "raw-messages", "all_messages_combined.json"), "r", encoding="utf-8") as f:
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data = json.load(f)
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feishu_re = re.compile(r"https?://[a-zA-Z0-9.-]+\.feishu\.cn/(?:wiki|docx)/[A-Za-z0-9]+")
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file_re = re.compile(r"\[文件\]\s+(.+?)\s+fileId:\s+(\S+)")
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kimi_re = re.compile(r"https?://[a-zA-Z0-9.-]+\.ok\.kimi\.link/\S*")
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weixin_re = re.compile(r"https?://mp\.weixin\.qq\.com/\S+")
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github_re = re.compile(r"https?://github\.com/\S+")
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# Person registry
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persons = {}
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# Link registry
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all_links = []
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# File registry
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all_files = []
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# Knowledge entities
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entities = []
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# Relationships
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relationships = []
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def get_or_create_person(name, dingtalk_id=None):
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if name not in persons:
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persons[name] = {"name": name, "dingtalk_id": dingtalk_id, "groups": [], "contributions": 0, "topics": set()}
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persons[name]["contributions"] += 1
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if dingtalk_id:
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persons[name]["dingtalk_id"] = dingtalk_id
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return persons[name]
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for group_name, msgs in data.items():
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group_label = "dc战略问题研究院" if group_name == "dc" else "创新组"
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for msg in msgs:
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content = msg.get("content", "")
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sender = msg.get("sender", "unknown")
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time = msg.get("createTime", "")
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sender_id = msg.get("senderOpenDingTalkId", "")
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person = get_or_create_person(sender, sender_id)
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if group_label not in person["groups"]:
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person["groups"].append(group_label)
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# Extract feishu links
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for link in feishu_re.findall(content):
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entry = {
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"url": link, "sender": sender, "time": time, "group": group_label,
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"type": "wiki" if "/wiki/" in link else "docx",
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"domain": link.split("/")[2]
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}
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# Try to get context from surrounding text
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ctx = content.replace(link, "").strip()[:100]
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if ctx:
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entry["context"] = ctx
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all_links.append(entry)
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# Extract file attachments
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fm = file_re.search(content)
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if fm:
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all_files.append({
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"name": fm.group(1), "fileId": fm.group(2),
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"sender": sender, "time": time, "group": group_label
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})
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# Extract Kimi links
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for link in kimi_re.findall(content):
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desc = content.split("https")[0].strip()[:100]
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all_links.append({
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"url": link, "sender": sender, "time": time, "group": group_label,
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"type": "kimi", "domain": "kimi.link", "context": desc
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})
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# Extract WeChat articles
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for link in weixin_re.findall(content):
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desc = content.split("https")[0].strip()[:100]
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all_links.append({
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"url": link, "sender": sender, "time": time, "group": group_label,
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"type": "weixin_article", "domain": "mp.weixin.qq.com", "context": desc
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})
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# Deduplicate links by URL
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unique_links = {}
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for link in all_links:
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url = link["url"]
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if url not in unique_links:
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unique_links[url] = {**link, "shared_count": 1}
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else:
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unique_links[url]["shared_count"] += 1
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# Build knowledge graph
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graph = {
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"metadata": {
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"generated": "2026-06-02",
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"groups": ["dc战略问题研究院", "创新组"],
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"time_range": "2026-05-01 ~ 2026-06-02",
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"total_messages": len(data["dc"]) + len(data["cx"])
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},
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"persons": {},
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"documents": [],
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"files": [],
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"topics": [],
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"relationships": []
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}
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# Process persons
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for name, p in persons.items():
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graph["persons"][name] = {
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"name": name,
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"groups": p["groups"],
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"contributions": p["contributions"],
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"dingtalk_id": p.get("dingtalk_id", "")
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}
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# Process documents (feishu links)
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domain_map = {
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"dianchukeji.feishu.cn": "dianchukeji (公司飞书)",
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"fcnlycv6dd0w.feishu.cn": "fcnlycv6dd0w (研究组飞书)",
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"ocnmca6f1o0p.feishu.cn": "ocnmca6f1o0p (另一飞书空间)",
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"my.feishu.cn": "my.feishu.cn (个人飞书)"
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}
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for url, link in unique_links.items():
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doc = {
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"url": url,
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"type": link["type"],
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"domain": domain_map.get(link["domain"], link["domain"]),
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"shared_by": link["sender"],
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"first_shared": link["time"],
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"group": link["group"],
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"context": link.get("context", ""),
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"share_count": link["shared_count"]
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}
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graph["documents"].append(doc)
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# Process files
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seen_files = set()
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for f in all_files:
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key = f["fileId"]
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if key not in seen_files:
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seen_files.add(key)
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graph["files"].append(f)
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# Build topic clusters
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# Group documents by sender and domain to identify topic clusters
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sender_domains = {}
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for doc in graph["documents"]:
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key = (doc["shared_by"], doc["domain"])
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if key not in sender_domains:
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sender_domains[key] = []
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sender_domains[key].append(doc)
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# Identify main topics based on content analysis
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topics = [
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{
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"name": "《我的花园世界》深度研究",
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"description": "围绕《我的花园世界》的全方位研究，包括产品机制、商业化、资源经济、竞品对比",
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"key_people": ["李志健", "黄静雯", "陈楚真", "傅明游"],
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"documents_count": 0,
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"files": ["主报告-我的花园世界.html", "2026-05-25_report_my_garden_world_us_project.html",
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"花园项目对策划的启示.md", "进一步的思考.md", "花园世界产品策略_GOS补充信息.md",
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"花园世界等相关产品25.10.28.xlsx"],
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"key_concepts": ["低压经营", "极致消耗", "非战斗GVG", "社交拉氪", "大通服", "腰部底座放大"]
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},
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{
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"name": "云湖工作室项目选择",
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"description": "广州云湖工作室第一个转型项目的方向选择分析",
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"key_people": ["黄静雯"],
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"documents_count": 0,
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"files": ["2026-05-yunhu-project-selection-strategy-report.html"],
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"key_concepts": ["2个月MVP", "AI美术适配", "素材CPI验证", "欧美化花园经营"]
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},
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{
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"name": "Game Analyst Agent 工具",
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"description": "通用产品分析自动化工具，自动探索游戏界面、提取机制与数值",
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"key_people": ["黄静雯"],
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"documents_count": 0,
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"files": ["game-analyst-agent-manual.html", "game-analyst-agent.zip"],
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"key_concepts": ["自动界面探索", "pHash去重", "9级决策链", "多游戏对比", "决策报告生成"]
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},
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{
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"name": "战略思维与方法论",
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"description": "李志健关于幸存者偏差、筹码管理、生长逻辑的战略反思",
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"key_people": ["李志健"],
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"documents_count": 0,
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"files": ["取败之道.md", "模型报告.html"],
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"key_concepts": ["幸存者偏差", "筹码管理", "正期望博弈", "生长逻辑", "非对称博弈"]
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},
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{
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"name": "AI落地与组织变革",
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"description": "AI在游戏研发中的实际落地、效率提升与组织变革讨论",
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"key_people": ["李志健", "林峰", "汪雄军", "上官成", "安东"],
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"documents_count": 0,
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"files": [],
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"key_concepts": ["AI军备竞赛", "端到端提效", "组织变革", "AI工作小组"]
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},
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{
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"name": "创新组日报与研发进展",
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"description": "创新组成员的每日工作日报，涵盖二合类游戏研发、AI工具使用",
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"key_people": ["王雨默", "韦译", "刘鹏", "卓泽", "徐锐"],
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"documents_count": 0,
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"files": ["工作流总结-王雨默.html"],
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"key_concepts": ["二合类游戏", "AI关卡生成", "UI编辑器", "数值平衡", "竞品分析"]
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},
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{
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"name": "战略组日报与知识产出",
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"description": "战略研究组成员每日提交的研究日报",
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"key_people": ["黄静雯", "陈楚真", "张家振", "夏莲", "莫润麟", "胡辉俊", "汪季"],
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"documents_count": 0,
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"files": [],
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"key_concepts": ["竞品拆解", "知识卡片", "MECH/REPORT/ORG卡片", "Agent架构"]
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}
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]
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graph["topics"] = topics
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# Build relationships
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rels = []
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# Person -> Topic relationships
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for topic in topics:
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for person in topic["key_people"]:
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rels.append({
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"source": person, "target": topic["name"],
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"type": "contributes_to", "weight": 1
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})
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# Person -> Person relationships (co-contribution in same group)
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for group_name, msgs in data.items():
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group_label = "dc战略问题研究院" if group_name == "dc" else "创新组"
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msg_senders = set(m.get("sender", "") for m in msgs)
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for p1 in msg_senders:
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for p2 in msg_senders:
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if p1 < p2:
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rels.append({
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"source": p1, "target": p2,
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"type": "co_group_member",
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"group": group_label, "weight": 1
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})
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graph["relationships"] = rels
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# Save graph
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with open(os.path.join(PROJECT_ROOT, "output", "knowledge-graph", "knowledge_graph.json"), "w", encoding="utf-8") as f:
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json.dump(graph, f, ensure_ascii=False, indent=2, default=str)
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# Print summary
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print("=== Knowledge Graph Summary ===")
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print("Persons: %d" % len(graph["persons"]))
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print("Documents (feishu links): %d" % len(graph["documents"]))
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print("Files: %d" % len(graph["files"]))
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print("Topics: %d" % len(graph["topics"]))
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print("Relationships: %d" % len(graph["relationships"]))
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print("\n=== Persons ===")
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for name, p in sorted(graph["persons"].items(), key=lambda x: -x[1]["contributions"]):
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print(" %s: %d msgs, groups=%s" % (name, p["contributions"], ", ".join(p["groups"])))
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print("\n=== Documents by Domain ===")
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domain_counts = {}
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for doc in graph["documents"]:
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d = doc["domain"]
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domain_counts[d] = domain_counts.get(d, 0) + 1
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for d, c in sorted(domain_counts.items(), key=lambda x: -x[1]):
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print(" %s: %d links" % (d, c))
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print("\n=== Files ===")
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for f in graph["files"]:
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print(" %s (by %s, %s)" % (f["name"], f["sender"], f["time"]))
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print("\n=== Topics ===")
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for t in graph["topics"]:
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print(" %s: %s" % (t["name"], t["description"][:60]))
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print(" Key people: %s" % ", ".join(t["key_people"]))
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print(" Key concepts: %s" % ", ".join(t["key_concepts"][:4]))
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print("\nSaved to knowledge_graph.json")
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