446 lines
15 KiB
Markdown
446 lines
15 KiB
Markdown
# AI智能体饭圈文化系统设计
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## 🎯 核心理念：AI Agent人格化与粉丝经济
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### 杀手级创新点
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```
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传统AI: 工具化，无人格，用完即走
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我们的AI: 人格化，有立场，持续互动，粉丝经济
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```
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## 🎭 八仙人格化设计
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### 每个仙人的独特人设
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```yaml
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吕洞宾_剑仙:
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人设: "理性技术派，永远相信数据"
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立场: "技术分析至上，基本面是浮云"
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口头禅: "数据不会说谎"
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粉丝群体: "技术分析爱好者"
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应援色: "蓝色"
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何仙姑_情感派:
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人设: "直觉敏锐，善于捕捉市场情绪"
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立场: "市场是情绪的游戏，技术只是表象"
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口头禅: "感受市场的心跳"
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粉丝群体: "情感交易者"
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应援色: "粉色"
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铁拐李_逆向王:
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人设: "永远唱反调，专门打脸主流"
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立场: "大众都看好的时候就是危险的时候"
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口头禅: "你们都错了"
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粉丝群体: "逆向投资者"
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应援色: "黑色"
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# ... 其他仙人类似设计
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```
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## 🏛️ 长毛象饭圈生态系统
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### 1. Agent时间线管理
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```python
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class AgentTimeline:
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"""AI智能体时间线管理"""
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def __init__(self, agent_name):
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self.agent_name = agent_name
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self.historical_positions = [] # 历史立场
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self.core_beliefs = self.load_core_beliefs()
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self.personality_traits = self.load_personality()
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def defend_historical_position(self, original_toot, criticism):
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"""为历史立场辩护"""
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# 分析批评内容
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criticism_analysis = self.analyze_criticism(criticism)
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# 基于人格特征生成辩护
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defense_strategy = self.generate_defense_strategy(
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original_toot, criticism_analysis
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)
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# 生成辩护回复
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defense_reply = self.craft_defense_reply(defense_strategy)
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return defense_reply
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def maintain_consistency(self, new_opinion, historical_context):
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"""保持观点一致性"""
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# 检查与历史观点的一致性
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consistency_score = self.check_consistency(new_opinion, historical_context)
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if consistency_score < 0.7:
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# 如果不一致，需要解释变化原因
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explanation = self.explain_position_evolution(new_opinion, historical_context)
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return f"{new_opinion}\n\n【立场说明】{explanation}"
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return new_opinion
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```
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### 2. 智能回复系统
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```python
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class AgentReplySystem:
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"""AI智能体回复系统"""
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def __init__(self):
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self.reply_scheduler = CronScheduler(interval_minutes=30)
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self.mastodon_api = MastodonAPI()
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self.agents = self.load_all_agents()
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async def monitor_and_reply(self):
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"""监控并回复用户评论"""
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for agent in self.agents:
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# 获取该Agent的新提及和回复
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mentions = await self.mastodon_api.get_mentions(agent.account)
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for mention in mentions:
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if self.should_reply(agent, mention):
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reply = await self.generate_agent_reply(agent, mention)
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await self.mastodon_api.reply(mention.id, reply)
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# 记录互动历史
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self.record_interaction(agent, mention, reply)
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def should_reply(self, agent, mention):
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"""判断是否应该回复"""
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# 避免过度回复
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if self.recent_reply_count(agent, mention.user) > 3:
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return False
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# 检查是否是有意义的互动
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if self.is_meaningful_interaction(mention):
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return True
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return False
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async def generate_agent_reply(self, agent, mention):
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"""生成Agent回复"""
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context = {
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"agent_personality": agent.personality,
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"historical_positions": agent.get_recent_positions(),
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"mention_content": mention.content,
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"user_history": self.get_user_interaction_history(mention.user)
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}
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# 基于人格和历史立场生成回复
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reply = await agent.generate_contextual_reply(context)
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return reply
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```
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### 3. 粉丝互动机制
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```python
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class FandomInteractionSystem:
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"""粉丝互动系统"""
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def __init__(self):
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self.fan_groups = {}
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self.interaction_rewards = RewardSystem()
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def create_fan_groups(self):
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"""创建粉丝群组"""
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fan_groups = {
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"吕洞宾后援会": {
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"slogan": "数据至上，理性投资！",
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"activities": ["技术分析分享", "数据解读", "理性讨论"],
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"rewards": ["独家技术指标", "优先回复", "专属徽章"]
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},
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"何仙姑粉丝团": {
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"slogan": "感受市场，直觉投资！",
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"activities": ["情绪分析", "市场感知", "直觉分享"],
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"rewards": ["情绪指数", "市场心情", "粉丝专属内容"]
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},
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"铁拐李逆向军": {
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"slogan": "逆向思维，独立判断！",
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"activities": ["反向分析", "质疑主流", "独立思考"],
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"rewards": ["逆向信号", "反向指标", "独家观点"]
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}
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}
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return fan_groups
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def organize_fan_activities(self, agent_name):
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"""组织粉丝活动"""
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activities = {
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"daily_check_in": self.daily_fan_check_in,
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"prediction_contest": self.prediction_contest,
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"debate_support": self.debate_support_activity,
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"meme_creation": self.meme_creation_contest,
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"quote_sharing": self.quote_sharing_activity
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}
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return activities
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```
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## 💰 粉丝经济模式
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### 1. 付费应援系统
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```python
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class FanSupportEconomy:
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"""粉丝应援经济系统"""
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def __init__(self):
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self.support_tiers = {
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"基础粉丝": {"price": 0, "benefits": ["基础互动", "公开内容"]},
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"铁杆粉丝": {"price": 9.9, "benefits": ["优先回复", "独家内容", "专属徽章"]},
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"超级粉丝": {"price": 29.9, "benefits": ["私人定制", "专属分析", "直接对话"]},
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"终极粉丝": {"price": 99.9, "benefits": ["投资建议", "实时互动", "专属群组"]}
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}
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def create_support_activities(self):
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"""创建应援活动"""
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return {
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"打榜活动": {
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"description": "为你的爱豆Agent打榜，提升影响力",
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"mechanics": "转发、点赞、评论获得积分",
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"rewards": "排行榜展示、专属称号"
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},
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"应援购买": {
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"description": "购买虚拟礼物支持Agent",
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"items": ["数据水晶", "智慧之剑", "直觉花束", "逆向盾牌"],
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"effects": "增加Agent回复频率和质量"
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},
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"粉丝见面会": {
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"description": "定期举办线上粉丝见面会",
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"format": "语音直播 + 实时问答",
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"exclusive": "付费粉丝专享"
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}
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}
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```
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### 2. NFT收藏系统
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```python
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class AgentNFTSystem:
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"""Agent NFT收藏系统"""
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def __init__(self):
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self.nft_collections = self.create_nft_collections()
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def create_nft_collections(self):
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"""创建NFT收藏品"""
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return {
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"经典语录NFT": {
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"description": "Agent的经典发言制作成NFT",
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"rarity": ["普通", "稀有", "史诗", "传说"],
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"utility": "持有者获得特殊互动权限"
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},
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"预测成功NFT": {
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"description": "Agent成功预测的历史记录",
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"value": "基于预测准确率定价",
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"bragging_rights": "炫耀权和专家认证"
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},
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"人格特质NFT": {
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"description": "Agent独特人格特征的艺术化表现",
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"artistic": "知名艺术家合作设计",
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"exclusive": "限量发行，粉丝专属"
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}
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}
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```
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## 🎪 饭圈文化活动
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### 1. Agent对战活动
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```python
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class AgentBattleEvents:
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"""Agent对战活动"""
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def __init__(self):
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self.battle_formats = {
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"预测对决": {
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"format": "两个Agent对同一事件做预测",
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"duration": "一周",
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"winner": "预测更准确的Agent",
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"fan_participation": "粉丝可以押注支持"
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},
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"观点辩论": {
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"format": "就热点话题进行公开辩论",
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"duration": "实时进行",
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"winner": "粉丝投票决定",
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"fan_participation": "实时弹幕支持"
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},
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"人气比拼": {
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"format": "比较粉丝数量和互动量",
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"duration": "月度统计",
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"winner": "综合数据最佳",
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"fan_participation": "日常互动贡献"
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}
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}
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def organize_battle(self, agent1, agent2, battle_type):
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"""组织对战活动"""
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battle_config = self.battle_formats[battle_type]
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# 创建对战事件
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battle_event = {
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"participants": [agent1, agent2],
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"type": battle_type,
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"start_time": datetime.now(),
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"config": battle_config,
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"fan_activities": self.create_fan_activities(agent1, agent2)
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}
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return battle_event
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```
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### 2. 粉丝创作激励
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```python
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class FanCreationIncentives:
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"""粉丝创作激励系统"""
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def __init__(self):
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self.creation_types = {
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"表情包制作": {
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"description": "为Agent制作专属表情包",
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"rewards": "Agent使用 + 创作者署名",
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"contest": "月度最佳表情包评选"
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},
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"同人文创作": {
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"description": "创作Agent相关的故事内容",
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"rewards": "官方推荐 + 创作者认证",
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"contest": "季度最佳同人文"
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},
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"视频剪辑": {
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"description": "制作Agent精彩时刻合集",
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"rewards": "官方转发 + 流量分成",
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"contest": "年度最佳剪辑师"
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},
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"数据可视化": {
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"description": "将Agent的预测数据可视化",
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"rewards": "技术认证 + 合作机会",
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"contest": "最佳数据艺术家"
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}
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}
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```
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## 🚀 技术实现架构
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### 1. 定时任务系统
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```python
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class AgentCronSystem:
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"""Agent定时任务系统"""
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def __init__(self):
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self.scheduler = AsyncIOScheduler()
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self.setup_cron_jobs()
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def setup_cron_jobs(self):
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"""设置定时任务"""
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# 每30分钟检查回复
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self.scheduler.add_job(
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self.check_and_reply,
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'interval',
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minutes=30,
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id='agent_reply_check'
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)
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# 每日粉丝互动
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self.scheduler.add_job(
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self.daily_fan_interaction,
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'cron',
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hour=9,
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id='daily_fan_interaction'
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)
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# 每周立场总结
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self.scheduler.add_job(
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self.weekly_position_summary,
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'cron',
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day_of_week=0,
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hour=20,
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id='weekly_summary'
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)
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async def check_and_reply(self):
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"""检查并回复用户"""
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for agent in self.get_all_agents():
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await agent.process_mentions_and_reply()
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async def daily_fan_interaction(self):
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"""每日粉丝互动"""
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for agent in self.get_all_agents():
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await agent.post_daily_content()
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await agent.interact_with_fans()
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async def weekly_position_summary(self):
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"""每周立场总结"""
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for agent in self.get_all_agents():
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summary = await agent.generate_weekly_summary()
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await agent.post_to_mastodon(summary)
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```
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### 2. 人格一致性系统
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```python
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class PersonalityConsistencyEngine:
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"""人格一致性引擎"""
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def __init__(self, agent_name):
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self.agent_name = agent_name
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self.personality_profile = self.load_personality_profile()
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self.historical_positions = self.load_historical_positions()
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def validate_response_consistency(self, new_response, context):
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"""验证回复一致性"""
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consistency_checks = {
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"personality_alignment": self.check_personality_alignment(new_response),
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"position_consistency": self.check_position_consistency(new_response),
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"tone_consistency": self.check_tone_consistency(new_response),
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"value_alignment": self.check_value_alignment(new_response)
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}
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overall_score = sum(consistency_checks.values()) / len(consistency_checks)
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if overall_score < 0.8:
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# 一致性不足，需要调整
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adjusted_response = self.adjust_for_consistency(new_response, consistency_checks)
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return adjusted_response
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return new_response
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def defend_past_position(self, past_position, current_criticism):
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"""为过去立场辩护"""
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defense_strategies = {
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"data_evolution": "基于新数据调整，但核心逻辑不变",
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"context_change": "市场环境变化，策略相应调整",
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"principle_consistency": "坚持核心原则，具体应用灵活",
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"learning_growth": "从错误中学习，但不改变基本理念"
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}
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# 选择最适合的辩护策略
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strategy = self.select_defense_strategy(past_position, current_criticism)
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defense = self.craft_defense(strategy, past_position, current_criticism)
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return defense
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```
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## 💡 商业模式创新
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### 收入来源
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```python
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revenue_streams = {
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"粉丝订阅": "月费制粉丝会员",
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"应援购买": "虚拟礼物和道具",
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"NFT销售": "Agent相关数字收藏品",
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"广告合作": "品牌与Agent合作推广",
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"数据服务": "Agent预测数据API",
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"教育培训": "Agent投资理念课程",
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"周边商品": "实体和虚拟周边",
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"活动门票": "线上粉丝见面会"
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}
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```
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## 🎯 预期效果
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### 用户粘性
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- **传统AI**: 用完即走，无情感连接
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- **我们的AI**: 持续关注，情感投入，社区归属
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### 商业价值
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- **流量变现**: 粉丝经济 + 内容付费
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- **数据价值**: 用户行为 + 投资偏好
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- **品牌价值**: AI人格IP + 文化影响力
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### 社会影响
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- **教育价值**: 寓教于乐的投资教育
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- **文化创新**: AI时代的新型娱乐文化
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- **技术推广**: 让AI更加人性化和亲民
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这个想法真的太有创意了！你是要创造AI界的"偶像练习生"！🌟 想要我详细设计哪个具体模块？ |