e4959d584f
主要更新： - 更新代理商端文档，明确项目由品牌方分配流程 - 新增Brief配置详情页(已配置)设计稿 - 完善工作台紧急待办中品牌新任务功能 - 整理Pencil设计文件中代理商端页面顺序 - 新增后端FastAPI框架及核心API - 新增前端Next.js页面和组件库 - 添加.gitignore排除构建和缓存文件 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
76 lines
2.4 KiB
Python
76 lines
2.4 KiB
Python
"""
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软性风控服务
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临界值、低置信度、历史记录触发警告
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"""
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from app.schemas.review import (
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SoftRiskContext,
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SoftRiskWarning,
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SoftRiskAction,
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)
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def evaluate_soft_risk(context: SoftRiskContext) -> list[SoftRiskWarning]:
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"""
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评估软性风控
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规则：
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- 违规率接近阈值（90% 以上）→ 二次确认
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- ASR/OCR 置信度 60%-80% → 备注提示
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- 有历史类似违规 → 备注提示
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Args:
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context: 软性风控上下文
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Returns:
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警告列表（可能为空）
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"""
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warnings: list[SoftRiskWarning] = []
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# 1. 临界值检测
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if (
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context.violation_rate is not None
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and context.violation_threshold is not None
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and context.violation_threshold > 0
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):
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ratio = context.violation_rate / context.violation_threshold
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# 使用 round 避免浮点数精度问题 (0.045/0.05 = 0.8999999999999999)
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ratio = round(ratio, 10)
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if ratio >= 0.9 and ratio < 1.0:
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warnings.append(SoftRiskWarning(
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code="NEAR_THRESHOLD",
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message=f"违规率 {context.violation_rate:.1%} 接近阈值 {context.violation_threshold:.1%}",
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action_required=SoftRiskAction.CONFIRM,
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blocking=False,
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))
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# 2. ASR 低置信度检测
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if context.asr_confidence is not None:
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if 0.6 <= context.asr_confidence < 0.8:
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warnings.append(SoftRiskWarning(
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code="LOW_CONFIDENCE_ASR",
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message=f"语音识别置信度较低 ({context.asr_confidence:.0%})，建议人工复核",
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action_required=SoftRiskAction.NOTE,
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blocking=False,
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))
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# 3. OCR 低置信度检测
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if context.ocr_confidence is not None:
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if 0.6 <= context.ocr_confidence < 0.8:
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warnings.append(SoftRiskWarning(
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code="LOW_CONFIDENCE_OCR",
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message=f"字幕识别置信度较低 ({context.ocr_confidence:.0%})，建议人工复核",
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action_required=SoftRiskAction.NOTE,
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blocking=False,
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))
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# 4. 历史违规检测
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if context.has_history_violation:
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warnings.append(SoftRiskWarning(
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code="HISTORY_RISK",
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message="该达人/内容存在历史类似违规记录",
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action_required=SoftRiskAction.NOTE,
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blocking=False,
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))
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return warnings
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