366 lines
9.7 KiB
Markdown
366 lines
9.7 KiB
Markdown
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# AI 垃圾评论检测系统架构
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## 系统概述
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本文档展示 Argon 主题的 AI 垃圾评论检测系统的完整架构和流程。
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## 主流程图
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[mermaid theme="default" width="100%" align="center"]
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flowchart TD
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Start([用户提交评论]) --> PreProcess[预处理评论]
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PreProcess --> CheckEnabled{启用 AI 检测?}
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CheckEnabled -->|否| DirectSave[直接保存评论]
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CheckEnabled -->|是| CheckMode{检测模式?}
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CheckMode -->|manual| DirectSave
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CheckMode -->|keyword| CheckKeyword[检查关键字]
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CheckMode -->|sample| CheckSample[智能抽样]
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CheckMode -->|all| NeedCheck[需要检测]
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CheckKeyword --> KeywordMatch{匹配关键字?}
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KeywordMatch -->|是| NeedCheck
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KeywordMatch -->|否| DirectSave
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CheckSample --> SampleDecision{抽中?}
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SampleDecision -->|是| NeedCheck
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SampleDecision -->|否| DirectSave
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NeedCheck --> SetPending[设置待审核状态]
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SetPending --> SaveWithFlag[保存评论并标记]
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SaveWithFlag --> TriggerDetect[触发 AI 检测]
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TriggerDetect --> CallAPI[调用 AI API]
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CallAPI --> ParseResult[解析结果]
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ParseResult --> CheckConfidence{置信度?}
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CheckConfidence -->|高| AutoHandle[自动处理]
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CheckConfidence -->|中| ManualReview[人工审核]
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CheckConfidence -->|低| MarkNormal[标记正常]
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AutoHandle --> CheckAction{处理方式?}
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CheckAction -->|trash| MoveTrash[移入回收站]
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CheckAction -->|hold| KeepPending[保持待审核]
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CheckAction -->|mark| JustMark[仅标记]
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MoveTrash --> NotifyAdmin[通知管理员]
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KeepPending --> NotifyAdmin
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JustMark --> SaveResult[保存结果]
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ManualReview --> SaveResult
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MarkNormal --> SaveResult
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NotifyAdmin --> SaveResult
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SaveResult --> AILearn{启用学习?}
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AILearn -->|是| ExtractKeywords[提取关键词]
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AILearn -->|否| End([结束])
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ExtractKeywords --> UpdateDB[更新词库]
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UpdateDB --> End
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DirectSave --> End
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style Start fill:#e1f5e1,stroke:#2e7d32,stroke-width:2px
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style End fill:#e1f5e1,stroke:#2e7d32,stroke-width:2px
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style MoveTrash fill:#ff6b6b,stroke:#c62828,stroke-width:2px
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style MarkNormal fill:#95e1d3,stroke:#2e7d32,stroke-width:2px
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style ManualReview fill:#ffa500,stroke:#ff8f00,stroke-width:2px
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[/mermaid]
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## 检测模式详解
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[mermaid theme="default" width="80%" align="center"]
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flowchart LR
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subgraph Manual[Manual 模式]
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M1[关闭实时检测]
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M2[仅手动扫描]
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end
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subgraph Keyword[Keyword 模式]
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K1[关键字触发]
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K2[精准检测]
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K3[低成本]
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end
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subgraph Sample[Sample 模式]
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S1[智能抽样]
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S2[平衡准确性]
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S3[控制成本]
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end
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subgraph All[All 模式]
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A1[全量检测]
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A2[最高准确性]
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A3[高成本]
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end
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style Manual fill:#e3f2fd
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style Keyword fill:#fff3e0
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style Sample fill:#f3e5f5
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style All fill:#fce4ec
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[/mermaid]
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## AI 检测流程
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[mermaid theme="default" width="90%"]
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sequenceDiagram
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participant User as 用户
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participant WP as WordPress
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participant Argon as Argon 主题
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participant AI as AI API
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participant DB as 数据库
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User->>WP: 提交评论
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WP->>Argon: 触发 preprocess_comment
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Argon->>Argon: 检查检测规则
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alt 需要检测
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Argon->>DB: 保存评论（待审核）
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Argon->>AI: 发送检测请求
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AI-->>Argon: 返回检测结果
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Argon->>Argon: 解析结果和置信度
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alt 高置信度垃圾评论
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Argon->>DB: 移入回收站
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Argon->>WP: 发送通知邮件
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else 中等置信度
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Argon->>DB: 标记待人工审核
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else 正常评论
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Argon->>DB: 标记为正常
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end
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Argon->>DB: 保存检测记录
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opt 启用 AI 学习
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Argon->>AI: 提取关键词
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AI-->>Argon: 返回关键词
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Argon->>DB: 更新学习词库
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end
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else 跳过检测
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Argon->>DB: 直接保存评论
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end
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WP-->>User: 显示提交结果
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[/mermaid]
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## 数据库结构
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[mermaid theme="default" width="85%"]
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erDiagram
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COMMENT ||--o{ COMMENT_META : has
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COMMENT {
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bigint comment_ID PK
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bigint comment_post_ID FK
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text comment_content
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varchar comment_author
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varchar comment_author_email
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varchar comment_author_IP
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datetime comment_date
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varchar comment_approved
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}
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COMMENT_META {
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bigint meta_id PK
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bigint comment_id FK
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varchar meta_key
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longtext meta_value
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}
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SPAM_DETECTION ||--|| COMMENT : detects
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SPAM_DETECTION {
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bigint id PK
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bigint comment_id FK
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varchar result
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float confidence
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text reason
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text keywords
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datetime detected_at
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varchar detection_code
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}
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LEARNED_KEYWORDS ||--o{ SPAM_DETECTION : learns_from
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LEARNED_KEYWORDS {
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bigint id PK
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varchar keyword
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int weight
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varchar category
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datetime created_at
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datetime updated_at
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}
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[/mermaid]
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## 系统状态机
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[mermaid theme="default" width="70%"]
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stateDiagram-v2
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[*] --> Submitted: 用户提交
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Submitted --> Pending: 需要检测
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Submitted --> Approved: 跳过检测
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Pending --> Detecting: 开始检测
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Detecting --> Analyzed: 检测完成
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Analyzed --> Spam: 高置信度垃圾
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Analyzed --> Suspicious: 中等置信度
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Analyzed --> Clean: 低置信度/正常
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Spam --> Trash: 自动处理
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Spam --> Hold: 保持待审核
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Spam --> Marked: 仅标记
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Suspicious --> ManualReview: 等待人工审核
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Clean --> Approved: 自动通过
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ManualReview --> Approved: 管理员批准
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ManualReview --> Trash: 管理员拒绝
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Hold --> Approved: 管理员批准
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Hold --> Trash: 管理员拒绝
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Marked --> Approved: 管理员批准
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Marked --> Trash: 管理员拒绝
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Trash --> [*]
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Approved --> [*]
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note right of Detecting
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调用 AI API
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解析返回结果
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计算置信度
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end note
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note right of ManualReview
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显示在后台
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等待管理员操作
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end note
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[/mermaid]
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## 性能优化策略
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[mermaid theme="default" width="95%"]
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flowchart TD
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subgraph Input[输入优化]
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I1[关键字预过滤]
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I2[白名单用户]
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I3[智能抽样]
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end
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subgraph Process[处理优化]
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P1[异步检测]
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P2[批量处理]
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P3[缓存结果]
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end
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subgraph API[API 优化]
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A1[Prompt 优化]
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A2[Token 控制]
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A3[超时处理]
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end
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subgraph Storage[存储优化]
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S1[索引优化]
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S2[定期清理]
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S3[归档历史]
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end
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Input --> Process
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Process --> API
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API --> Storage
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style Input fill:#e8f5e9
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style Process fill:#e3f2fd
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style API fill:#fff3e0
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style Storage fill:#f3e5f5
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[/mermaid]
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## 配置建议矩阵
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[mermaid theme="default" width="100%"]
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graph TB
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subgraph Small[小型博客 < 100评论/天]
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S1[检测模式: keyword]
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S2[Prompt: 标准模式]
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S3[置信度阈值: > 0.9]
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S4[自动处理: trash]
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end
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subgraph Medium[中型博客 100-500评论/天]
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M1[检测模式: sample 30%]
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M2[Prompt: 标准模式]
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M3[置信度阈值: > 0.85]
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M4[自动处理: hold]
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end
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subgraph Large[大型博客 > 500评论/天]
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L1[检测模式: sample 40%]
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L2[Prompt: 极简模式]
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L3[置信度阈值: > 0.8]
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L4[自动处理: mark]
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L5[定期批量扫描]
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end
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style Small fill:#c8e6c9
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style Medium fill:#fff9c4
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style Large fill:#ffccbc
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[/mermaid]
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## 错误处理流程
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[mermaid theme="default" width="85%"]
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flowchart TD
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Start([检测开始]) --> CallAPI[调用 AI API]
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CallAPI --> CheckResponse{响应状态?}
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CheckResponse -->|成功| ParseJSON[解析 JSON]
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CheckResponse -->|超时| Timeout[超时处理]
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CheckResponse -->|错误| APIError[API 错误]
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ParseJSON --> ValidateData{数据有效?}
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ValidateData -->|是| ProcessResult[处理结果]
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ValidateData -->|否| DataError[数据错误]
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Timeout --> RetryCheck{重试次数?}
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RetryCheck -->|< 3| Retry[重试请求]
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RetryCheck -->|>= 3| FallbackPending[降级：待审核]
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Retry --> CallAPI
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APIError --> LogError[记录错误日志]
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DataError --> LogError
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LogError --> NotifyAdmin[通知管理员]
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NotifyAdmin --> FallbackPending
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ProcessResult --> SaveResult[保存结果]
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FallbackPending --> SaveResult
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SaveResult --> End([结束])
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style Timeout fill:#ffccbc
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style APIError fill:#ffccbc
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style DataError fill:#ffccbc
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style FallbackPending fill:#fff9c4
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style ProcessResult fill:#c8e6c9
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[/mermaid]
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## 总结
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通过以上流程图，我们可以清晰地看到：
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1. **主流程**：从用户提交到最终处理的完整流程
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2. **检测模式**：四种模式的特点和适用场景
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3. **时序交互**：各组件之间的交互顺序
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4. **数据结构**：数据库表关系和字段定义
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5. **状态机**：评论的各种状态转换
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6. **性能优化**：多层次的优化策略
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7. **配置建议**：不同规模博客的推荐配置
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8. **错误处理**：完善的异常处理机制
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这个系统设计充分考虑了：
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- ✅ 准确性：多级置信度判断
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- ✅ 性能：异步处理、智能抽样
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- ✅ 成本：灵活的检测模式
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- ✅ 可靠性：完善的错误处理
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- ✅ 可维护性：清晰的架构设计
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