微爱帮监狱寄信AI识别五大核心技术-平芜编程栈

一、敏感内容智能识别

class ContentSafetyAI: """信件内容安全检测引擎""" def __init__(self): # 多模型融合检测 self.models = { 'violence': self.load_model('violence_v3'), 'escape': self.load_model('escape_plan_v2'), 'contraband': self.load_model('contraband_v4'), 'coordination': self.load_model('gang_coordination'), 'suicide': self.load_model('suicide_risk_v3') } # 监狱专用词库 self.prison_lexicon = { 'locations': ['监区', '放风场', '车间', '食堂', '医务室'], 'staff': ['管教', '狱警', '队长', '指导员'], 'restricted': ['工具', '药品', '现金', '手机', '刀具'] } async def analyze_letter(self, text_content): # 1. 上下文理解 context = self.extract_context(text_content) # 2. 多层检测 risk_scores = {} for risk_type, model in self.models.items(): score = await model.predict(text_content, context) risk_scores[risk_type] = score # 3. 特殊规则检测 prison_risks = self.check_prison_rules(text_content) # 4. 综合评分 final_score = self.calculate_risk_score(risk_scores, prison_risks) return { 'risk_level': self.classify_risk(final_score), 'details': risk_scores, 'violations': prison_risks.get('violations', []), 'recommendation': self.get_recommendation(final_score) }

二、情绪状态预警系统

class EmotionRiskAI: """服刑人员情绪风险评估""" def analyze_emotional_state(self, text, writing_patterns): # 1. 情感强度分析 emotion_scores = { 'anger': self.detect_anger_indicators(text), 'depression': self.assess_depression_signals(text), 'anxiety': self.measure_anxiety_level(text), 'hopelessness': self.evaluate_hopelessness(text) } # 2. 写作模式分析 pattern_analysis = { 'handwriting_pressure': writing_patterns.get('pressure', 0), 'writing_speed_change': self.calculate_speed_variance(writing_patterns), 'repetition_frequency': self.count_repetitions(text), 'self_reference_rate': self.analyze_self_references(text) } # 3. 危机预警 crisis_indicators = [ self.check_suicide_ideation(text), self.detect_self_harm_intent(text), self.identify_violent_fantasies(text) ] return { 'emotional_profile': emotion_scores, 'behavioral_indicators': pattern_analysis, 'crisis_warnings': [ind for ind in crisis_indicators if ind], 'risk_score': self.calculate_emotional_risk(emotion_scores) }

三、笔迹身份验证

class HandwritingVerificationAI: """笔迹身份确认系统""" def verify_identity(self, current_sample, historical_samples): # 1. 特征提取 features = { 'slant_angle': self.measure_slant(current_sample), 'letter_spacing': self.calculate_spacing(current_sample), 'pressure_pattern': self.analyze_pressure(current_sample), 'stroke_dynamics': self.extract_stroke_features(current_sample) } # 2. 比对分析 similarity_scores = [] for historical in historical_samples: score = self.compare_handwriting(features, historical) similarity_scores.append(score) # 3. 多因素决策 confidence = self.calculate_confidence(similarity_scores) # 4. 异常检测 anomalies = self.detect_anomalies(features, historical_samples) return { 'is_authentic': confidence > 0.85, 'confidence_score': confidence, 'anomalies_detected': anomalies, 'verification_method': 'multi_feature_analysis' }

四、通信模式分析

class CommunicationPatternAI: """信件通信模式智能分析""" def analyze_communication_patterns(self, letters_data): patterns = { # 频率分析 'frequency_changes': self.detect_frequency_anomalies(letters_data), # 内容模式 'content_evolution': self.track_content_changes(letters_data), # 网络分析 'social_network': self.map_communication_network(letters_data), # 时间模式 'temporal_patterns': self.analyze_timing_patterns(letters_data), # 隐蔽通信检测 'covert_signals': self.detect_covert_communication(letters_data) } # 风险评估 risks = [] if patterns['frequency_changes'].get('abnormal'): risks.append('异常通信频率') if patterns['covert_signals']: risks.append('疑似隐蔽通信') return { 'patterns': patterns, 'risk_indicators': risks, 'recommended_monitoring': self.suggest_monitoring_level(patterns) }

五、图像内容审核

class ImageContentAI: """信件附件图像安全审核""" async def inspect_images(self, images): findings = [] for img in images: # 1. 违禁物品检测 prohibited_items = await self.detect_prohibited_items(img) # 2. 敏感符号识别 sensitive_symbols = self.identify_symbols(img) # 3. 隐写分析 steganalysis = await self.check_for_steganography(img) # 4. 文字提取与审核 extracted_text = self.extract_and_analyze_text(img) findings.append({ 'image_id': img.id, 'prohibited_items': prohibited_items, 'sensitive_symbols': sensitive_symbols, 'steganalysis_result': steganalysis, 'text_findings': extracted_text, 'approval_status': self.determine_approval([ prohibited_items, sensitive_symbols, steganalysis ]) }) return findings