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