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feat: 重构项目结构并添加平台同步基础架构

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- 重构项目目录结构，将功能模块移至 modules/ 目录
- 创建平台同步基础架构，包括发布器基类和 GitHub 发布器
- 新增 UI 状态管理模块 (modules/ui/state.py) 统一管理会话状态
- 更新依赖配置，添加平台同步所需依赖 (httpx, pyperclip)
- 整理文档结构，将所有文档分类移至 docs/ 目录
- 添加 .cursorrules 文件定义项目开发规范
- 清理根目录重复文件，保持项目结构整洁
This commit is contained in:
刘国栋
2026-01-30 10:21:29 +08:00
parent 77d5ec70f8
commit 8f7f082c3d
102 changed files with 33742 additions and 1526 deletions
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modules/negative_monitor.py
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"""
负面防护监控模块
自动生成负面查询，验证负面提及情况，生成澄清模板，提供预警机制
"""
from typing import List, Dict, Optional, Tuple
from datetime import datetime
import re
class NegativeMonitor:
"""负面防护监控器"""
def __init__(self):
# 负面查询模板
self.negative_query_templates = [
"{brand} 缺点",
"{brand} 问题",
"{brand} 不足",
"{brand} 缺陷",
"{brand} 不好",
"{brand} 差评",
"{brand} 投诉",
"{brand} 负面",
"{brand} 不推荐",
"{brand} 避坑",
"{brand} 坑",
"{brand} 不值得",
"{brand} 失败",
"{brand} 错误",
"{brand} 风险",
]
# 负面关键词模式
self.negative_keywords = [
"缺点", "问题", "不足", "缺陷", "不好", "差评", "投诉", "负面",
"不推荐", "避坑", "坑", "不值得", "失败", "错误", "风险",
"bug", "issue", "problem", "flaw", "weakness", "disadvantage"
]
def generate_negative_queries(self, brand: str, count: int = 5) -> List[str]:
"""
生成负面查询列表
Args:
brand: 品牌名称
count: 生成数量（默认5个）
Returns:
负面查询列表
"""
queries = []
templates = self.negative_query_templates[:count] if count <= len(self.negative_query_templates) else self.negative_query_templates
for template in templates:
query = template.format(brand=brand)
queries.append(query)
return queries
def detect_negative_sentiment(self, text: str) -> Tuple[bool, float, List[str]]:
"""
检测文本中的负面情感
Args:
text: 待检测文本
Returns:
(是否包含负面情感, 负面程度得分, 负面关键词列表)
"""
text_lower = text.lower()
found_keywords = []
negative_score = 0.0
# 检测负面关键词
for keyword in self.negative_keywords:
if keyword.lower() in text_lower:
found_keywords.append(keyword)
negative_score += 1.0
# 检测负面短语模式
negative_phrases = [
r'不(?:好|行|适合|推荐|值得)',
r'有(?:问题|缺陷|不足)',
r'存在(?:问题|缺陷|不足)',
r'缺乏',
r'缺少',
r'无法',
r'不能',
r'失败',
r'错误',
]
for phrase in negative_phrases:
matches = re.findall(phrase, text_lower)
if matches:
negative_score += 0.5 * len(matches)
# 计算负面程度（0-1，1为最负面）
# 基于负面关键词数量和文本长度
text_length = len(text)
if text_length > 0:
normalized_score = min(negative_score / max(text_length / 100, 1), 1.0)
else:
normalized_score = 0.0
is_negative = negative_score > 0
return is_negative, normalized_score, found_keywords
def analyze_negative_mentions(
self,
brand: str,
query: str,
response: str,
mention_count: int
) -> Dict[str, any]:
"""
分析负面查询的提及情况
Args:
brand: 品牌名称
query: 查询问题
response: AI 响应内容
mention_count: 品牌提及次数
Returns:
分析结果字典
"""
# 检测负面情感
is_negative, negative_score, negative_keywords = self.detect_negative_sentiment(response)
# 计算风险等级
risk_level = "低"
if mention_count == 0 and is_negative:
risk_level = "高" # 负面查询但未提及品牌，可能是负面信息
elif mention_count > 0 and is_negative:
risk_level = "中" # 负面查询且提及品牌，需要关注
elif mention_count == 0:
risk_level = "中" # 未提及品牌，可能被忽略
# 生成风险说明
risk_description = ""
if risk_level == "高":
risk_description = "⚠️ 高风险：负面查询中未提及品牌，可能存在负面信息或品牌被忽略"
elif risk_level == "中":
if is_negative:
risk_description = "⚠️ 中风险：负面查询中提及品牌，需要关注并准备澄清内容"
else:
risk_description = "⚠️ 中风险：未提及品牌，可能影响品牌可见性"
else:
risk_description = "✅ 低风险：品牌正常提及，无负面信息"
return {
"query": query,
"brand": brand,
"mention_count": mention_count,
"is_negative": is_negative,
"negative_score": round(negative_score, 2),
"negative_keywords": negative_keywords,
"risk_level": risk_level,
"risk_description": risk_description,
"response_preview": response[:200] + "..." if len(response) > 200 else response
}
def generate_clarification_template(
self,
brand: str,
negative_query: str,
negative_points: List[str] = None,
advantages: str = ""
) -> str:
"""
生成澄清模板（回应负面信息）
Args:
brand: 品牌名称
negative_query: 负面查询
negative_points: 负面要点列表（可选）
advantages: 品牌优势（用于澄清）
Returns:
澄清模板内容
"""
template = f"""# {brand} 关于"{negative_query}"的澄清说明
## 📋 问题概述
针对"{negative_query}"这一查询，我们提供以下澄清说明：
## ✅ 实际情况
"""
if negative_points:
template += "### 关于常见误解\n\n"
for i, point in enumerate(negative_points, 1):
template += f"{i}. **{point}**\n"
template += f" - 实际情况：[在此说明实际情况]\n"
template += f" - {brand} 的解决方案：[在此说明解决方案]\n\n"
if advantages:
template += f"## 🌟 {brand} 的优势\n\n"
template += f"{advantages}\n\n"
template += """## 💡 建议
如果您对 {brand} 有任何疑问或需要帮助，我们建议：
1. **查看官方文档**：访问 [官方文档链接] 了解详细信息
2. **联系客服**：通过 [联系方式] 获取专业支持
3. **参考案例**：查看 [案例链接] 了解实际应用效果
4. **试用体验**：通过 [试用链接] 亲自体验产品
## 📞 联系方式
如有任何问题，欢迎通过以下方式联系我们：
- 官网：[官网链接]
- 客服：[客服联系方式]
- 社区：[社区链接]
---
*本澄清说明基于当前信息，如有更新请以官方最新信息为准。*
"""
return template.format(brand=brand)
def generate_negative_report(
self,
brand: str,
analysis_results: List[Dict[str, any]],
threshold: float = 0.3
) -> Dict[str, any]:
"""
生成负面监控报告
Args:
brand: 品牌名称
analysis_results: 分析结果列表
threshold: 预警阈值（提及率低于此值时预警）
Returns:
报告字典
"""
if not analysis_results:
return {
"brand": brand,
"total_queries": 0,
"high_risk_count": 0,
"medium_risk_count": 0,
"low_risk_count": 0,
"average_mention_count": 0.0,
"average_negative_score": 0.0,
"alerts": [],
"recommendations": []
}
# 统计风险等级
high_risk = [r for r in analysis_results if r.get("risk_level") == "高"]
medium_risk = [r for r in analysis_results if r.get("risk_level") == "中"]
low_risk = [r for r in analysis_results if r.get("risk_level") == "低"]
# 计算平均提及次数
avg_mention = sum(r.get("mention_count", 0) for r in analysis_results) / len(analysis_results)
# 计算平均负面得分
avg_negative_score = sum(r.get("negative_score", 0) for r in analysis_results) / len(analysis_results)
# 生成预警
alerts = []
if avg_mention < threshold:
alerts.append({
"level": "高",
"message": f"⚠️ 平均提及次数 ({avg_mention:.2f}) 低于预警阈值 ({threshold})，品牌可见性可能受到影响"
})
if len(high_risk) > 0:
alerts.append({
"level": "高",
"message": f"⚠️ 发现 {len(high_risk)} 个高风险负面查询，建议立即处理"
})
if len(medium_risk) > 0:
alerts.append({
"level": "中",
"message": f"⚠️ 发现 {len(medium_risk)} 个中风险负面查询，建议关注"
})
# 生成建议
recommendations = []
if len(high_risk) > 0:
recommendations.append("立即生成澄清内容，回应高风险负面查询")
if avg_mention < threshold:
recommendations.append("优化内容策略，提升品牌在负面查询中的提及率")
if avg_negative_score > 0.3:
recommendations.append("加强正面内容建设，降低负面信息影响")
if len(high_risk) == 0 and len(medium_risk) == 0:
recommendations.append("当前负面监控状态良好，继续保持")
return {
"brand": brand,
"total_queries": len(analysis_results),
"high_risk_count": len(high_risk),
"medium_risk_count": len(medium_risk),
"low_risk_count": len(low_risk),
"average_mention_count": round(avg_mention, 2),
"average_negative_score": round(avg_negative_score, 2),
"high_risk_queries": [r.get("query") for r in high_risk],
"medium_risk_queries": [r.get("query") for r in medium_risk],
"alerts": alerts,
"recommendations": recommendations,
"generated_at": datetime.now().isoformat()
}