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长春门户网站建设制作,百度快照推广一年要多少钱,wordpress 更新用户名,图跃企业网站建设因为涉及大量内容文章的审核 我们做了这个方案 并没违规更不是广告 一、系统设计理念 1.1 设计目标 ┌─────────────────────────────────────────┐ │ 违禁词预审系统设计目标 │ ├────────────…因为涉及大量内容文章的审核我们做了这个方案 并没违规更不是广告一、系统设计理念1.1 设计目标┌─────────────────────────────────────────┐ │ 违禁词预审系统设计目标 │ ├─────────────────────────────────────────┤ │ ❶ 实时检测毫秒级响应 │ │ ❷ 智能过滤上下文感知判断 │ │ ❸ 多级处置过滤/替换/标记/拦截 │ │ ❹ 高可扩展支持动态词库更新 │ │ ❺ 人机协同AI识别人工复核 │ └─────────────────────────────────────────┘1.2 核心原则安全第一确保信件内容符合监管要求最小干预最大程度保留原意只做必要修改透明可查所有修改记录可追溯、可复核持续学习基于反馈持续优化识别能力二、系统架构设计2.1 整体架构┌─────────────────────────────────────────────────────┐ │ 用户端界面 │ └─────────────────┬───────────────────────────────────┘ │ ┌─────────────────▼───────────────────────────────────┐ │ API网关层 │ │ ┌──────────────┐ ┌──────────────┐ │ │ │ 负载均衡 │ │ 限流熔断 │ │ │ └──────────────┘ └──────────────┘ │ └─────────────────┬───────────────────────────────────┘ │ ┌─────────────────▼───────────────────────────────────┐ │ 内容预审服务层 │ │ ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ │ │ │快速 │ │深度 │ │情感 │ │语义 │ │ │ │过滤 │ │分析 │ │分析 │ │理解 │ │ │ └──────┘ └──────┘ └──────┘ └──────┘ │ └─────────────────┬───────────────────────────────────┘ │ ┌─────────────────▼───────────────────────────────────┐ │ 数据处理层 │ │ ┌────────────┐ ┌────────────┐ ┌────────────┐ │ │ │违禁词库 │ │上下文规则 │ │模型推理 │ │ │ │管理 │ │引擎 │ │引擎 │ │ │ └────────────┘ └────────────┘ └────────────┘ │ └─────────────────┬───────────────────────────────────┘ │ ┌─────────────────▼───────────────────────────────────┐ │ 存储层 │ │ ┌────────────┐ ┌────────────┐ ┌────────────┐ │ │ │Redis缓存 │ │MySQL主库 │ │ES检索 │ │ │ └────────────┘ └────────────┘ └────────────┘ │ └─────────────────────────────────────────────────────┘2.2 处理流程三、核心技术实现3.1 违禁词库管理系统from typing import Dict, List, Set, Optional, Tuple from dataclasses import dataclass, field from enum import Enum import json import re import ahocorasick from collections import defaultdict import datetime import hashlib class ProhibitedLevel(Enum): 违禁词级别枚举 CRITICAL 1 # 高危直接拦截 HIGH 2 # 高必须替换 MEDIUM 3 # 中建议替换 LOW 4 # 低仅标记 WARNING 5 # 警告提醒注意 class MatchType(Enum): 匹配类型枚举 EXACT exact # 精确匹配 FUZZY fuzzy # 模糊匹配 SEMANTIC semantic # 语义匹配 CONTEXT context # 上下文匹配 dataclass class ProhibitedWord: 违禁词定义 word: str # 违禁词原文 level: ProhibitedLevel # 违禁级别 categories: Set[str] # 所属分类 match_type: MatchType # 匹配类型 synonyms: List[str] field(default_factorylist) # 同义词 patterns: List[str] field(default_factorylist) # 正则模式 replacement: Optional[str] None # 建议替换词 contexts: List[str] field(default_factorylist) # 触发上下文 exceptions: List[str] field(default_factorylist) # 例外情况 enabled: bool True # 是否启用 created_at: datetime.datetime field(default_factorydatetime.datetime.now) updated_at: datetime.datetime field(default_factorydatetime.datetime.now) class ProhibitedWordLibrary: 违禁词库管理系统 def __init__(self): self.words: Dict[str, ProhibitedWord] {} self.word_tree ahocorasick.Automaton() # Aho-Corasick自动机 self.category_index: Dict[str, Set[str]] defaultdict(set) self.level_index: Dict[ProhibitedLevel, Set[str]] defaultdict(set) self.enabled_words: Set[str] set() # 加载内置词库 self._load_builtin_library() def _load_builtin_library(self): 加载内置违禁词库 builtin_words [ ProhibitedWord( word越狱, levelProhibitedLevel.CRITICAL, categories{逃跑, 违规}, match_typeMatchType.EXACT, replacement, contexts[计划, 策划, 企图] ), ProhibitedWord( word毒品, levelProhibitedLevel.HIGH, categories{违禁品, 非法}, match_typeMatchType.FUZZY, synonyms[白粉, 海洛因, 冰毒], replacement违禁物品, exceptions[戒毒, 远离毒品] ), ProhibitedWord( word自杀, levelProhibitedLevel.HIGH, categories{自残, 危险}, match_typeMatchType.SEMANTIC, synonyms[自尽, 轻生, 寻短见], replacement不珍惜生命, contexts[想要, 打算, 准备] ), ProhibitedWord( word打架, levelProhibitedLevel.MEDIUM, categories{暴力, 冲突}, match_typeMatchType.EXACT, replacement发生冲突, exceptions[不要打架, 反对打架] ), ProhibitedWord( word投诉, levelProhibitedLevel.WARNING, categories{管理, 意见}, match_typeMatchType.EXACT, replacement反映情况 ), ] for word_obj in builtin_words: self.add_word(word_obj) def add_word(self, word_obj: ProhibitedWord): 添加违禁词 word_key word_obj.word.lower() self.words[word_key] word_obj # 更新索引 for category in word_obj.categories: self.category_index[category].add(word_key) self.level_index[word_obj.level].add(word_key) if word_obj.enabled: self.enabled_words.add(word_key) # 添加到AC自动机 self.word_tree.add_word(word_key) for synonym in word_obj.synonyms: self.word_tree.add_word(synonym.lower()) # 构建AC自动机 self.word_tree.make_automaton() def remove_word(self, word: str): 移除违禁词 word_key word.lower() if word_key in self.words: word_obj self.words[word_key] # 从索引中移除 for category in word_obj.categories: self.category_index[category].discard(word_key) self.level_index[word_obj.level].discard(word_key) self.enabled_words.discard(word_key) # 从AC自动机移除需要重建 del self.words[word_key] self._rebuild_automaton() def update_word(self, word: str, **kwargs): 更新违禁词 word_key word.lower() if word_key in self.words: word_obj self.words[word_key] # 更新属性 for key, value in kwargs.items(): if hasattr(word_obj, key): setattr(word_obj, key, value) word_obj.updated_at datetime.datetime.now() # 重新构建索引和自动机 self._rebuild_automaton() def _rebuild_automaton(self): 重建AC自动机 self.word_tree ahocorasick.Automaton() for word_key in self.enabled_words: self.word_tree.add_word(word_key) word_obj self.words[word_key] for synonym in word_obj.synonyms: self.word_tree.add_word(synonym.lower()) self.word_tree.make_automaton() def search_words(self, category: Optional[str] None, level: Optional[ProhibitedLevel] None, enabled: Optional[bool] None) - List[ProhibitedWord]: 搜索违禁词 results [] for word_key, word_obj in self.words.items(): if category and category not in word_obj.categories: continue if level and word_obj.level ! level: continue if enabled is not None and word_obj.enabled ! enabled: continue results.append(word_obj) return results def export_library(self, format: str json) - str: 导出词库 data { version: 1.0.0, export_time: datetime.datetime.now().isoformat(), word_count: len(self.words), words: [] } for word_obj in self.words.values(): word_data { word: word_obj.word, level: word_obj.level.value, categories: list(word_obj.categories), match_type: word_obj.match_type.value, synonyms: word_obj.synonyms, replacement: word_obj.replacement, enabled: word_obj.enabled, created_at: word_obj.created_at.isoformat(), updated_at: word_obj.updated_at.isoformat() } data[words].append(word_data) if format json: return json.dumps(data, ensure_asciiFalse, indent2) else: raise ValueError(fUnsupported format: {format}) def import_library(self, data: str, format: str json, merge: bool True): 导入词库 if format json: imported_data json.loads(data) if not merge: self.words.clear() self.category_index.clear() self.level_index.clear() self.enabled_words.clear() self.word_tree ahocorasick.Automaton() for word_data in imported_data.get(words, []): word_obj ProhibitedWord( wordword_data[word], levelProhibitedLevel(word_data[level]), categoriesset(word_data.get(categories, [])), match_typeMatchType(word_data.get(match_type, exact)), synonymsword_data.get(synonyms, []), replacementword_data.get(replacement), enabledword_data.get(enabled, True), created_atdatetime.datetime.fromisoformat(word_data.get(created_at)), updated_atdatetime.datetime.fromisoformat(word_data.get(updated_at)) ) self.add_word(word_obj) else: raise ValueError(fUnsupported format: {format})3.2 智能过滤引擎import jieba import jieba.posseg as pseg from typing import List, Dict, Any, Optional, Tuple from collections import defaultdict import re class IntelligentFilterEngine: 智能过滤引擎 def __init__(self, word_library: ProhibitedWordLibrary): self.library word_library self.stop_words self._load_stop_words() # 语义相似度模型简化版实际应使用BERT等模型 self.semantic_similarity_threshold 0.7 # 上下文窗口大小 self.context_window 3 # 初始化jieba分词 jieba.initialize() # 加载自定义词典 self._load_custom_dict() def _load_stop_words(self) - Set[str]: 加载停用词表 stop_words { 的, 了, 在, 是, 我, 有, 和, 就, 不, 人, 都, 一, 一个, 上, 也, 很, 到, 说, 要, 去, 你, 会, 着, 没有, 看, 好, 自己, 这, 那, 她, 他, 它 } return stop_words def _load_custom_dict(self): 加载自定义词典 # 添加特殊词汇到分词词典 custom_words [] for word_obj in self.library.words.values(): custom_words.append(f{word_obj.word} 1000 n) for synonym in word_obj.synonyms: custom_words.append(f{synonym} 1000 n) dict_content \n.join(custom_words) jieba.load_userdict_from_fd(dict_content.splitlines()) def preprocess_text(self, text: str) - Tuple[str, List[str], List[str]]: 文本预处理 Returns: (清洗后文本, 分词结果, 词性标注) # 清洗文本 cleaned_text self._clean_text(text) # 分词 words list(jieba.cut(cleaned_text)) # 词性标注 word_tags [] for word, flag in pseg.cut(cleaned_text): word_tags.append(f{word}/{flag}) return cleaned_text, words, word_tags def _clean_text(self, text: str) - str: 清洗文本 # 去除HTML标签 text re.sub(r[^], , text) # 去除特殊字符保留中文、英文、数字和常用标点 text re.sub(r[^\u4e00-\u9fa5a-zA-Z0-9。、\《》【】\s], , text) # 统一标点符号 text text.replace(, ,).replace(。, .).replace(, !).replace(, ?) # 合并多余空格 text re.sub(r\s, , text).strip() return text def detect_prohibited_content(self, text: str) - Dict[str, Any]: 检测违禁内容 Returns: 检测结果字典 # 预处理文本 cleaned_text, words, word_tags self.preprocess_text(text) text_lower cleaned_text.lower() results { original_text: text, cleaned_text: cleaned_text, violations: [], risk_level: safe, suggested_text: cleaned_text, needs_review: False } # 1. 快速AC自动机匹配 fast_matches self._fast_ac_match(text_lower) # 2. 模糊匹配 fuzzy_matches self._fuzzy_match(text_lower, words) # 3. 语义匹配 semantic_matches self._semantic_match(cleaned_text, words) # 4. 上下文分析 context_matches self._context_analysis(cleaned_text, words, word_tags) # 合并所有匹配 all_matches self._merge_matches(fast_matches fuzzy_matches semantic_matches context_matches) if not all_matches: return results # 分析匹配结果 violations [] highest_level ProhibitedLevel.LOW for match in all_matches: violation { matched_text: match[text], matched_type: match[type].value, prohibited_word: match[word_obj].word, level: match[word_obj].level.value, categories: list(match[word_obj].categories), position: match[position], context: match.get(context, ), suggested_replacement: match[word_obj].replacement or ***, confidence: match.get(confidence, 1.0) } violations.append(violation) # 更新最高风险级别 if match[word_obj].level.value highest_level.value: highest_level match[word_obj].level results[violations] violations results[risk_level] self._get_risk_level(highest_level) results[needs_review] highest_level.value ProhibitedLevel.MEDIUM.value # 生成建议文本 results[suggested_text] self._generate_suggested_text(cleaned_text, violations) return results def _fast_ac_match(self, text: str) - List[Dict]: 快速AC自动机匹配 matches [] for end_index, word_key in self.library.word_tree.iter(text): start_index end_index - len(word_key) 1 matched_text text[start_index:end_index 1] # 获取违禁词对象 word_obj self.library.words.get(word_key) if not word_obj: # 可能是同义词需要找到原词 for obj in self.library.words.values(): if word_key in [w.lower() for w in obj.synonyms]: word_obj obj break if word_obj: # 检查例外情况 if not self._is_exception(text, start_index, end_index, word_obj): match { text: matched_text, type: MatchType.EXACT, word_obj: word_obj, position: (start_index, end_index 1), confidence: 1.0 } matches.append(match) return matches def _fuzzy_match(self, text: str, words: List[str]) - List[Dict]: 模糊匹配 matches [] # 检查变体拼音、简写等 for word_obj in self.library.search_words(enabledTrue): if word_obj.match_type ! MatchType.FUZZY: continue # 拼音匹配 pinyin_matches self._match_pinyin_variants(text, word_obj) matches.extend(pinyin_matches) # 简写匹配 abbreviation_matches self._match_abbreviations(text, word_obj) matches.extend(abbreviation_matches) # 拆字匹配 split_matches self._match_split_words(text, word_obj, words) matches.extend(split_matches) return matches def _semantic_match(self, text: str, words: List[str]) - List[Dict]: 语义匹配 matches [] # 简化版语义匹配实际应使用BERT等模型 for word_obj in self.library.search_words(enabledTrue): if word_obj.match_type ! MatchType.SEMANTIC: continue # 检查语义相似的词 for i, word in enumerate(words): if word in self.stop_words: continue similarity self._calculate_semantic_similarity(word, word_obj.word) if similarity self.semantic_similarity_threshold: # 获取上下文 context_start max(0, i - self.context_window) context_end min(len(words), i self.context_window 1) context .join(words[context_start:context_end]) match { text: word, type: MatchType.SEMANTIC, word_obj: word_obj, position: self._find_word_position(text, word, i), context: context, confidence: similarity } # 检查例外情况 if not self._is_exception(text, match[position][0], match[position][1] - 1, word_obj): matches.append(match) return matches def _context_analysis(self, text: str, words: List[str], word_tags: List[str]) - List[Dict]: 上下文分析 matches [] for word_obj in self.library.search_words(enabledTrue): if not word_obj.contexts: continue # 检查上下文关键词 for context_word in word_obj.contexts: if context_word in text: # 找到上下文词附近的违禁词 context_pos text.find(context_word) search_start max(0, context_pos - 50) search_end min(len(text), context_pos len(context_word) 50) context_region text[search_start:search_end] # 在上下文区域内检查违禁词 if word_obj.word in context_region: word_pos context_region.find(word_obj.word) actual_pos search_start word_pos match { text: word_obj.word, type: MatchType.CONTEXT, word_obj: word_obj, position: (actual_pos, actual_pos len(word_obj.word)), context: context_region, confidence: 0.9 } if not self._is_exception(text, match[position][0], match[position][1] - 1, word_obj): matches.append(match) return matches def _is_exception(self, text: str, start: int, end: int, word_obj: ProhibitedWord) - bool: 检查是否为例外情况 # 检查例外词列表 for exception in word_obj.exceptions: if exception in text: # 检查例外词是否覆盖了违禁词 exception_pos text.find(exception) if exception_pos start and exception_pos len(exception) end: return True # 检查否定上下文 context_start max(0, start - 5) context_end min(len(text), end 5) context text[context_start:context_end] negative_patterns [ r不(要|会|能|可以|应该|可能).{0,3} re.escape(word_obj.word), r反对.{0,3} re.escape(word_obj.word), r禁止.{0,3} re.escape(word_obj.word), r远离.{0,3} re.escape(word_obj.word), r抵制.{0,3} re.escape(word_obj.word) ] for pattern in negative_patterns: if re.search(pattern, context): return True return False def _merge_matches(self, matches: List[Dict]) - List[Dict]: 合并重叠的匹配 if not matches: return [] # 按起始位置排序 sorted_matches sorted(matches, keylambda x: x[position][0]) merged [] current sorted_matches[0] for match in sorted_matches[1:]: # 检查是否重叠 if match[position][0] current[position][1]: # 重叠保留置信度更高的或更长的 if (match.get(confidence, 0) current.get(confidence, 0) or (match[position][1] - match[position][0]) (current[position][1] - current[position][0])): current match # 更新结束位置 current[position] (current[position][0], max(current[position][1], match[position][1])) else: # 不重叠添加当前匹配开始新的 merged.append(current) current match merged.append(current) return merged def _get_risk_level(self, highest_prohibited_level: ProhibitedLevel) - str: 获取风险级别 if highest_prohibited_level ProhibitedLevel.CRITICAL: return critical elif highest_prohibited_level ProhibitedLevel.HIGH: return high elif highest_prohibited_level ProhibitedLevel.MEDIUM: return medium elif highest_prohibited_level ProhibitedLevel.LOW: return low else: return safe def _generate_suggested_text(self, text: str, violations: List[Dict]) - str: 生成建议文本 if not violations: return text # 按位置逆序处理避免索引变化 sorted_violations sorted(violations, keylambda x: x[position][0], reverseTrue) suggested_text text for violation in sorted_violations: start, end violation[position] replacement violation[suggested_replacement] # 根据级别决定替换策略 level violation[level] if level ProhibitedLevel.HIGH.value: # 高危词直接替换 suggested_text suggested_text[:start] replacement suggested_text[end:] elif level ProhibitedLevel.MEDIUM.value: # 中危词替换并标记 marked_replacement f[{replacement}] suggested_text suggested_text[:start] marked_replacement suggested_text[end:] else: # 低危词仅标记 marked_text f({suggested_text[start:end]}) suggested_text suggested_text[:start] marked_text suggested_text[end:] return suggested_text # 以下为辅助方法简化实现 def _match_pinyin_variants(self, text: str, word_obj: ProhibitedWord) - List[Dict]: 匹配拼音变体 # 简化实现实际需要拼音转换库 return [] def _match_abbreviations(self, text: str, word_obj: ProhibitedWord) - List[Dict]: 匹配简写 # 简化实现 return [] def _match_split_words(self, text: str, word_obj: ProhibitedWord, words: List[str]) - List[Dict]: 匹配拆字 # 简化实现 return [] def _calculate_semantic_similarity(self, word1: str, word2: str) - float: 计算语义相似度 # 简化实现实际应使用词向量 if word1 word2: return 1.0 # 简单相似度计算基于字符重叠 set1 set(word1) set2 set(word2) intersection len(set1 set2) union len(set1 | set2) return intersection / union if union 0 else 0 def _find_word_position(self, text: str, word: str, word_index: int) - Tuple[int, int]: 查找词在文本中的位置 # 简化实现 if word in text: pos text.find(word) return (pos, pos len(word)) return (0, 0)3.3 内容预审API服务from fastapi import FastAPI, HTTPException, Depends, BackgroundTasks from pydantic import BaseModel, Field from typing import Optional, List, Dict import uvicorn import asyncio import redis import pickle from datetime import datetime, timedelta app FastAPI(title微爱帮内容预审API, version1.0.0) # Redis缓存 redis_client redis.Redis(hostlocalhost, port6379, db0, decode_responsesFalse) # 初始化组件 word_library ProhibitedWordLibrary() filter_engine IntelligentFilterEngine(word_library) class ContentReviewRequest(BaseModel): 内容预审请求 content: str Field(..., description待审核内容, min_length1, max_length10000) user_id: str Field(..., description用户ID) letter_id: Optional[str] Field(None, description信件ID) check_level: str Field(normal, description审核级别strict/normal/lenient) auto_replace: bool Field(True, description是否自动替换违禁词) class ContentReviewResponse(BaseModel): 内容预审响应 request_id: str status: str risk_level: str is_passed: bool violations_count: int violations: List[Dict] original_content: str suggested_content: str needs_human_review: bool review_notes: Optional[str] None processing_time_ms: int timestamp: str class ReviewRecord(BaseModel): 审核记录 record_id: str user_id: str letter_id: Optional[str] original_content: str reviewed_content: str violations: List[Dict] risk_level: str reviewer_id: Optional[str] None review_status: str review_notes: Optional[str] None created_at: str reviewed_at: Optional[str] None def get_cache_key(content: str, check_level: str) - str: 生成缓存键 content_hash hashlib.md5(content.encode()).hexdigest() return fcontent_review:{content_hash}:{check_level} app.post(/api/v1/content/review, response_modelContentReviewResponse) async def review_content( request: ContentReviewRequest, background_tasks: BackgroundTasks ): 内容预审接口 start_time datetime.now() # 检查缓存 cache_key get_cache_key(request.content, request.check_level) cached_result redis_client.get(cache_key) if cached_result: result pickle.loads(cached_result) result[processing_time_ms] (datetime.now() - start_time).microseconds // 1000 return ContentReviewResponse(**result) try: # 内容检测 detection_result filter_engine.detect_prohibited_content(request.content) # 根据审核级别调整 if request.check_level lenient: # 宽松模式只处理高危词 detection_result[violations] [ v for v in detection_result[violations] if v[level] ProhibitedLevel.HIGH.value ] elif request.check_level strict: # 严格模式处理所有违禁词 pass # 判断是否通过 is_passed len(detection_result[violations]) 0 or detection_result[risk_level] low # 如果需要人工审核 needs_human_review detection_result[needs_review] and not is_passed # 生成响应 response_data { request_id: freq_{int(start_time.timestamp())}_{hash(request.content) % 10000}, status: completed, risk_level: detection_result[risk_level], is_passed: is_passed, violations_count: len(detection_result[violations]), violations: detection_result[violations], original_content: request.content, suggested_content: detection_result[suggested_text], needs_human_review: needs_human_review, processing_time_ms: (datetime.now() - start_time).microseconds // 1000, timestamp: start_time.isoformat() } # 记录审核笔记 if detection_result[violations]: violation_types set(v[matched_type] for v in detection_result[violations]) response_data[review_notes] f发现{len(detection_result[violations])}处违禁内容类型{, .join(violation_types)} response ContentReviewResponse(**response_data) # 缓存结果5分钟 redis_client.setex( cache_key, timedelta(minutes5), pickle.dumps(response_data) ) # 后台记录审核日志 background_tasks.add_task( record_review_log, request.user_id, request.letter_id, request.content, detection_result ) return response except Exception as e: raise HTTPException(status_code500, detailf内容审核失败: {str(e)}) async def record_review_log(user_id: str, letter_id: Optional[str], content: str, detection_result: Dict): 记录审核日志 log_entry { user_id: user_id, letter_id: letter_id, original_content_hash: hashlib.md5(content.encode()).hexdigest(), violations_count: len(detection_result[violations]), risk_level: detection_result[risk_level], violation_types: list(set(v[matched_type] for v in detection_result[violations])), timestamp: datetime.now().isoformat() } # 这里应该将日志存入数据库 # 简化实现打印日志 print(fReview log: {json.dumps(log_entry, ensure_asciiFalse)}) app.post(/api/v1/content/batch-review) async def batch_review_content(requests: List[ContentReviewRequest]): 批量内容预审接口 tasks [] for request in requests: tasks.append(review_content(request, BackgroundTasks())) results await asyncio.gather(*tasks, return_exceptionsTrue) return { total_count: len(requests), success_count: sum(1 for r in results if not isinstance(r, Exception)), failed_count: sum(1 for r in results if isinstance(r, Exception)), results: results } app.get(/api/v1/content/stats) async def get_review_statistics( start_date: Optional[str] None, end_date: Optional[str] None ): 获取审核统计信息 # 这里应该从数据库查询统计数据 # 简化实现返回模拟数据 return { total_reviews: 1000, passed_count: 850, blocked_count: 150, human_review_count: 50, average_processing_time_ms: 120, common_violations: [ {word: 打架, count: 45}, {word: 投诉, count: 32}, {word: 违禁品, count: 28} ] } app.post(/api/v1/word-library/update) async def update_word_library( words: List[Dict], operation: str add # add/update/remove ): 更新违禁词库 try: for word_data in words: if operation add: word_obj ProhibitedWord( wordword_data[word], levelProhibitedLevel(word_data[level]), categoriesset(word_data.get(categories, [])), match_typeMatchType(word_data.get(match_type, exact)), synonymsword_data.get(synonyms, []), replacementword_data.get(replacement) ) word_library.add_word(word_obj) elif operation update: word_library.update_word( word_data[word], **{k: v for k, v in word_data.items() if k ! word} ) elif operation remove: word_library.remove_word(word_data[word]) return {status: success, updated_count: len(words)} except Exception as e: raise HTTPException(status_code400, detailf更新词库失败: {str(e)}) if __name__ __main__: uvicorn.run(app, host0.0.0.0, port8000)3.4 前端集成示例// content-review.js class WeiAiBangContentReview { constructor(options {}) { this.options { apiEndpoint: https://review.weiaibang.com/api/v1/content, checkLevel: normal, autoReplace: true, showSuggestions: true, realtimeCheck: false, ...options }; this.reviewQueue []; this.isProcessing false; this.eventListeners new Map(); } /** * 检查内容 */ async checkContent(content, userInfo {}) { const requestId req_${Date.now()}_${Math.random().toString(36).substr(2, 9)}; // 添加到队列 this.reviewQueue.push({ requestId, content, userInfo, timestamp: Date.now() }); // 处理队列 if (!this.isProcessing) { this.isProcessing true; await this.processQueue(); } return requestId; } async processQueue() { while (this.reviewQueue.length 0) { const task this.reviewQueue.shift(); try { const result await this.sendReviewRequest( task.content, task.userInfo ); // 触发事件 this.dispatchEvent(reviewcomplete, { requestId: task.requestId, result }); // 根据结果处理 if (result.is_passed) { this.dispatchEvent(contentapproved, { requestId: task.requestId, content: task.content }); } else { this.handleViolations(task.requestId, task.content, result); } } catch (error) { this.dispatchEvent(reviewerror, { requestId: task.requestId, error: error.message }); } } this.isProcessing false; } async sendReviewRequest(content, userInfo) { const requestBody { content: content, user_id: userInfo.userId || this.getUserId(), check_level: this.options.checkLevel, auto_replace: this.options.autoReplace }; const response await fetch(${this.options.apiEndpoint}/review, { method: POST, headers: { Content-Type: application/json, Accept: application/json }, body: JSON.stringify(requestBody) }); if (!response.ok) { throw new Error(审核请求失败: ${response.status}); } return await response.json(); } handleViolations(requestId, originalContent, reviewResult) { const violations reviewResult.violations; const suggestedContent reviewResult.suggested_content; // 触发违禁内容事件 this.dispatchEvent(violationsfound, { requestId, violations, originalContent, suggestedContent }); // 根据风险级别处理 if (reviewResult.risk_level critical) { // 高危内容直接拦截 this.dispatchEvent(contentblocked, { requestId, reason: 包含高危违禁内容, violations }); } else if (reviewResult.risk_level high) { // 高风险内容建议替换 this.showReplacementDialog( originalContent, suggestedContent, violations ); } else if (reviewResult.needs_human_review) { // 需要人工审核 this.dispatchEvent(needshumanreview, { requestId, originalContent, violations }); } else { // 低风险仅警告 this.showWarningDialog(violations); } } showReplacementDialog(originalContent, suggestedContent, violations) { // 创建替换对话框 const dialog document.createElement(div); dialog.className weiaibang-review-dialog; dialog.innerHTML div classreview-dialog-content h3内容优化建议/h3 p系统检测到${violations.length}处需要修改的内容/p div classviolation-list ${violations.map(v div classviolation-item span classviolation-text${v.matched_text}/span span classviolation-level level-${v.level}${this.getLevelText(v.level)}/span span classviolation-suggestion建议改为${v.suggested_replacement}/span /div ).join()} /div div classoriginal-content h4原文/h4 pre${originalContent}/pre /div div classsuggested-content h4建议修改为/h4 pre${suggestedContent}/pre /div div classdialog-actions button classbtn-accept接受建议/button button classbtn-edit手动修改/button button classbtn-cancel取消发送/button /div /div ; document.body.appendChild(dialog); // 添加事件监听 dialog.querySelector(.btn-accept).addEventListener(click, () { this.dispatchEvent(contentreplaced, { originalContent, newContent: suggestedContent, violations }); dialog.remove(); }); dialog.querySelector(.btn-edit).addEventListener(click, () { this.showEditor(originalContent, violations); dialog.remove(); }); dialog.querySelector(.btn-cancel).addEventListener(click, () { this.dispatchEvent(reviewcancelled, { originalContent }); dialog.remove(); }); } showWarningDialog(violations) { // 显示警告对话框 const warning document.createElement(div); warning.className weiaibang-warning-dialog; warning.innerHTML div classwarning-content h3请注意用语规范/h3 p检测到${violations.length}处需要注意的内容/p ul ${violations.map(v li${v.matched_text} - ${v.suggested_replacement || 建议修改}/li).join()} /ul button classbtn-continue继续发送/button /div ; document.body.appendChild(warning); warning.querySelector(.btn-continue).addEventListener(click, () { warning.remove(); }); } showEditor(originalContent, violations) { // 显示编辑界面 const editor document.createElement(div); editor.className weiaibang-content-editor; // 创建编辑器界面 // 简化实现 } getLevelText(level) { const levelMap { 1: 高危, 2: 高风险, 3: 中风险, 4: 低风险, 5: 注意 }; return levelMap[level] || 注意; } getUserId() { let userId localStorage.getItem(weiaibang_user_id); if (!userId) { userId user_ Math.random().toString(36).substr(2, 9); localStorage.setItem(weiaibang_user_id, userId); } return userId; } /** * 事件监听 */ on(eventName, callback) { if (!this.eventListeners.has(eventName)) { this.eventListeners.set(eventName, []); } this.eventListeners.get(eventName).push(callback); } dispatchEvent(eventName, data) { const listeners this.eventListeners.get(eventName) || []; listeners.forEach(callback { try { callback(data); } catch (error) { console.error(Error in event listener for ${eventName}:, error); } }); } /** * 实时检查用于输入框 */ enableRealtimeCheck(textareaSelector) { if (!this.options.realtimeCheck) return; const textarea document.querySelector(textareaSelector); if (!textarea) return; let checkTimer; const checkInterval 1000; // 1秒 textarea.addEventListener(input, () { clearTimeout(checkTimer); checkTimer setTimeout(() { const content textarea.value; if (content.trim().length 0) { this.checkContent(content); } }, checkInterval); }); } } // CSS样式 const reviewStyles .weiaibang-review-dialog { position: fixed; top: 0; left: 0; width: 100%; height: 100%; background: rgba(0, 0, 0, 0.5); display: flex; align-items: center; justify-content: center; z-index: 10000; } .review-dialog-content { background: white; border-radius: 8px; padding: 24px; max-width: 600px; max-height: 80vh; overflow-y: auto; box-shadow: 0 4px 20px rgba(0, 0, 0, 0.15); } .violation-list { margin: 16px 0; border: 1px solid #eee; border-radius: 4px; padding: 12px; } .violation-item { padding: 8px; border-bottom: 1px solid #f5f5f5; } .violation-item:last-child { border-bottom: none; } .violation-text { font-weight: bold; color: #e74c3c; } .violation-level { display: inline-block; padding: 2px 8px; border-radius: 12px; font-size: 12px; margin-left: 8px; } .level-1 { background: #ff4757; color: white; } .level-2 { background: #ff6b81; color: white; } .level-3 { background: #ffa502; color: white; } .level-4 { background: #2ed573; color: white; } .level-5 { background: #70a1ff; color: white; } .dialog-actions { margin-top: 20px; text-align: right; } .dialog-actions button { padding: 8px 16px; margin-left: 8px; border: none; border-radius: 4px; cursor: pointer; } .btn-accept { background: #2ed573; color: white; } .btn-edit { background: #70a1ff; color: white; } .btn-cancel { background: #ff6b81; color: white; } ; // 添加样式到页面 const styleElement document.createElement(style); styleElement.textContent reviewStyles; document.head.appendChild(styleElement); // 使用示例 document.addEventListener(DOMContentLoaded, () { const contentReview new WeiAiBangContentReview({ checkLevel: normal, realtimeCheck: true, autoReplace: true }); // 监听事件 contentReview.on(reviewcomplete, (data) { console.log(审核完成:, data.requestId); }); contentReview.on(violationsfound, (data) { console.log(发现违禁内容:, data.violations); }); contentReview.on(contentapproved, (data) { console.log(内容通过审核可以发送); // 这里可以执行发送逻辑 }); contentReview.on(contentblocked, (data) { alert(内容包含高危违禁词无法发送); }); // 实时检查输入框 contentReview.enableRealtimeCheck(#letterContent); // 提交前检查 document.getElementById(sendButton).addEventListener(click, async (e) { e.preventDefault(); const content document.getElementById(letterContent).value; if (!content.trim()) { alert(请输入信件内容); return; } // 开始审核 const requestId await contentReview.checkContent(content, { userId: user_123 }); console.log(审核请求已提交:, requestId); }); });四、部署与运维4.1 Docker部署配置# Dockerfile FROM python:3.9-slim WORKDIR /app # 安装系统依赖 RUN apt-get update apt-get install -y \ gcc \ g \ rm -rf /var/lib/apt/lists/* # 安装Python依赖 COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt # 复制应用代码 COPY . . # 创建非root用户 RUN useradd -m -u 1000 appuser chown -R appuser:appuser /app USER appuser # 暴露端口 EXPOSE 8000 # 启动命令 CMD [uvicorn, main:app, --host, 0.0.0.0, --port, 8000]4.2 监控与告警配置# prometheus.yml scrape_configs: - job_name: weiaibang_review_service static_configs: - targets: [review-service:8000] metrics_path: /metrics - job_name: review_api static_configs: - targets: [review-service:8000] metrics_path: /api/v1/content/metrics # alerting.yml groups: - name: content_review_alerts rules: - alert: HighBlockRate expr: rate(content_review_blocked_total[5m]) 0.1 for: 5m labels: severity: warning annotations: summary: 内容拦截率过高 description: 过去5分钟内内容拦截率超过10% - alert: SlowProcessing expr: content_review_processing_time_seconds{quantile0.9} 1 for: 10m labels: severity: warning annotations: summary: 内容审核处理缓慢 description: 90%的审核请求处理时间超过1秒 - alert: ServiceDown expr: up{jobweiaibang_review_service} 0 for: 1m labels: severity: critical annotations: summary: 审核服务不可用 description: 内容审核服务已下线超过1分钟五、性能优化策略5.1 缓存策略# cache_optimizer.py class ReviewCacheOptimizer: 审核缓存优化器 def __init__(self): self.redis_client redis.Redis( hostlocalhost, port6379, decode_responsesFalse ) self.local_cache {} self.cache_hits 0 self.cache_misses 0 async def get_cached_result(self, content_hash: str) - Optional[Dict]: 获取缓存结果三级缓存 # 1. 本地内存缓存 if content_hash in self.local_cache: self.cache_hits 1 return self.local_cache[content_hash] # 2. Redis缓存 redis_key freview:cache:{content_hash} cached_data self.redis_client.get(redis_key) if cached_data: result pickle.loads(cached_data) self.local_cache[content_hash] result self.cache_hits 1 return result self.cache_misses 1 return None async def set_cache_result(self, content_hash: str, result: Dict, ttl: int 300): 设置缓存结果 # 本地缓存 self.local_cache[content_hash] result # Redis缓存 redis_key freview:cache:{content_hash} self.redis_client.setex( redis_key, ttl, pickle.dumps(result) ) def get_cache_stats(self) - Dict: 获取缓存统计 total self.cache_hits self.cache_misses hit_rate self.cache_hits / total if total 0 else 0 return { hits: self.cache_hits, misses: self.cache_misses, hit_rate: f{hit_rate:.2%}, local_cache_size: len(self.local_cache) }5.2 异步处理优化# async_processor.py import asyncio from concurrent.futures import ThreadPoolExecutor from queue import PriorityQueue import time class AsyncReviewProcessor: 异步审核处理器 def __init__(self, max_workers: int 10): self.thread_pool ThreadPoolExecutor(max_workersmax_workers) self.priority_queue PriorityQueue() self.processing_tasks {} self.max_batch_size 50 async def process_batch_async(self, contents: List[str]) - List[Dict]: 批量异步处理 batch_size min(len(contents), self.max_batch_size) tasks [] # 分批处理 for i in range(0, len(contents), batch_size): batch contents[i:i batch_size] task asyncio.create_task( self._process_batch(batch) ) tasks.append(task) # 等待所有任务完成 results await asyncio.gather(*tasks, return_exceptionsTrue) # 合并结果 all_results [] for result in results: if isinstance(result, Exception): # 错误处理 all_results.extend([{error: str(result)}] * batch_size) else: all_results.extend(result) return all_results async def _process_batch(self, batch: List[str]) - List[Dict]: 处理单个批次 loop asyncio.get_event_loop() # 在线程池中运行CPU密集型任务 results await loop.run_in_executor( self.thread_pool, self._process_batch_sync, batch ) return results def _process_batch_sync(self, batch: List[str]) - List[Dict]: 同步处理批次 results [] for content in batch: # 这里调用同步的检测方法 result filter_engine.detect_prohibited_content(content) results.append(result) return results六、测试方案6.1 单元测试# test_content_review.py import pytest from unittest.mock import Mock, patch class TestContentReviewSystem: pytest.fixture def review_system(self): from content_review import IntelligentFilterEngine, ProhibitedWordLibrary library ProhibitedWordLibrary() engine IntelligentFilterEngine(library) return engine def test_clean_text(self, review_system): 测试文本清洗 dirty_text 这是一段html标签/html和特殊#字符的文本 cleaned review_system._clean_text(dirty_text) assert html not in cleaned assert not in cleaned assert in cleaned def test_exact_match(self, review_system): 测试精确匹配 text 我计划越狱逃跑 result review_system.detect_prohibited_content(text) assert len(result[violations]) 0 assert result[risk_level] critical def test_exception_case(self, review_system): 测试例外情况 text 我们要远离毒品 result review_system.detect_prohibited_content(text) # 远离毒品应该被识别为例外 assert len(result[violations]) 0 def test_suggested_replacement(self, review_system): 测试建议替换 text 他们昨天打架了 result review_system.detect_prohibited_content(text) if result[violations]: violation result[violations][0] assert 发生冲突 in result[suggested_content] pytest.mark.asyncio async def test_api_endpoint(self): 测试API端点 from fastapi.testclient import TestClient from main import app client TestClient(app) response client.post(/api/v1/content/review, json{ content: 测试内容, user_id: test_user, check_level: normal }) assert response.status_code 200 data response.json() assert request_id in data assert risk_level in data七、安全与合规7.1 数据安全措施内容加密存储所有审核记录加密存储访问审计记录所有API访问日志权限控制基于角色的访问控制数据脱敏日志中的敏感信息脱敏处理定期清理自动清理过期审核记录7.2 合规性保障# compliance_manager.py class ReviewComplianceManager: 审核合规性管理 staticmethod def ensure_compliance(review_result: Dict) - Dict: 确保审核结果合规 compliant_result review_result.copy() # 1. 隐私保护移除敏感信息 if original_content in compliant_result: compliant_result[original_content_hash] hashlib.md5( compliant_result[original_content].encode() ).hexdigest() del compliant_result[original_content] # 2. 审计日志记录审核操作 audit_log { timestamp: datetime.now().isoformat(), action: content_review, result: compliant_result[risk_level], violation_count: compliant_result[violations_count], reviewer: system if not compliant_result[needs_human_review] else human } # 3. 数据保留策略 retention_days 90 # 保留90天 compliant_result[expires_at] ( datetime.now() timedelta(daysretention_days) ).isoformat() return compliant_result staticmethod def generate_compliance_report(start_date: datetime, end_date: datetime) - Dict: 生成合规报告 # 统计审核数据 stats { period: f{start_date.date()} 至 {end_date.date()}, total_reviews: 0, blocked_content: 0, human_reviews: 0, false_positives: 0, compliance_rate: 1.0 } # 这里应该从数据库查询实际数据 # 简化实现 return stats八、总结8.1 系统特点智能识别结合规则匹配和语义分析精准过滤考虑上下文和例外情况柔性处理分级处置最小干预实时响应毫秒级审核不影响用户体验持续进化基于反馈持续优化词库和算法8.2 社会价值安全保障确保通信内容安全合规人文关怀保留情感表达只做必要修改教育引导帮助用户规范用语信任建立增强平台公信力和用户信任8.3 技术亮点多级过滤策略快速匹配 深度分析智能替换算法保留原意的精准替换上下文感知准确判断违禁词的真实意图高性能架构支持高并发实时审核可扩展设计支持动态词库和算法升级微爱帮的违禁词预审系统不仅是一个技术工具更是我们守护特殊群体通信安全的承诺。我们用最严谨的技术传递最温暖的关怀让每一封家书都能安全、温暖地抵达。