141 lines
5.8 KiB
Python
141 lines
5.8 KiB
Python
"""Deterministic Markdown reports with escaped untrusted text and explicit data boundaries."""
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import html
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from datetime import date
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from app.schemas.integration_risk import IntegrationRiskRequest
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from app.schemas.risk_prediction import RiskAssessmentResult
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from app.services.database_risk_scoring import normalize_category, normalize_material
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from app.services.purchase_orders import calendar_year_window
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from app.services.reports import LEVEL_LABELS
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def md_text(value: object) -> str:
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if value is None or value == "":
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return "未提供"
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value = html.escape(str(value), quote=False).replace("\r", " ").replace("\n", " ")
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for char in ("\\", "`", "*", "_", "[", "]", "|", "#", "!"):
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value = value.replace(char, "\\" + char)
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return value
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def render_markdown_report(
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payload: IntegrationRiskRequest,
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result: RiskAssessmentResult,
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as_of: date,
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operator_name: str,
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) -> str:
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product = payload.assessment
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start, _ = calendar_year_window(as_of, 3)
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info = (
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("评估对象", product.product_name or product.subject_id),
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("对象编号", product.subject_id),
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("请求编号", payload.request_id),
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("提交品类", product.product_category),
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("评分品类", normalize_category(product.product_category) or "无法归类，品类维度使用默认 50 分"),
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("规格", product.specification),
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("包装原文", product.material_type),
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("评分物料标签", normalize_material(product.material_type)),
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("供应商", product.supplier),
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("加工厂", product.factory),
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("开发阶段", product.stage_new),
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("已知缺陷等级", product.defect_level),
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("评估人", operator_name),
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("数据截止日期", as_of),
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("评估背景", product.description),
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)
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lines = ["# 新品风险评估报告", "", "## 一、评估信息", "", "| 项目 | 内容 |", "|---|---|"]
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lines.extend(f"| {label} | {md_text(value)} |" for label, value in info)
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lines.extend(
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[
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"",
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"## 二、综合风险结论",
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"",
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f"- 综合安全得分：{result.score:.2f}（越高风险越低）",
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f"- 风险等级：{LEVEL_LABELS[result.level.value]}",
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f"- 模型版本：{md_text(result.model_version)}",
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]
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)
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if result.alert_text:
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lines.append(f"- 数据与建议提示：{md_text(result.alert_text)}")
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lines.extend(
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[
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"",
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"## 三、七维风险评分",
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"",
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"| 风险维度 | 得分 | 权重 | 数据状态 | 说明 |",
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"|---|---:|---:|---|---|",
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]
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)
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lines.extend(
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f"| {md_text(item.dimension)} | {item.score:.2f} | {item.weight:g}% | "
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f"{md_text(item.status)} | {md_text(item.reason)} |"
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for item in result.dimension_scores
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)
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lines.extend(["", "## 四、历史统计与相似异常案例", ""])
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if result.category_warning:
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lines.append(f"- 评分品类历史异常：{result.category_warning.anomaly_count} 条。")
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if result.supplier_freq:
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freq = result.supplier_freq
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lines.extend(
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[
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f"- 供应商：{md_text(freq.supplier_name)}；合作次数：{md_text(freq.cooperation_count)}；"
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f"历史异常：{md_text(freq.anomaly_count)}。",
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f"- 供应商统计说明：{md_text(freq.note)}",
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]
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)
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lines.extend(["", "| 日期 | 产品 | 异常现象 | 原因 | 历史处理方案 | 相似度 |", "|---|---|---|---|---|---:|"])
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for case in result.similar_cases:
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similarity = f"{case.similarity:.1%}" if case.similarity is not None else "未提供"
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lines.append(
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"| "
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+ " | ".join(
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md_text(value)
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for value in (
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case.date,
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case.product,
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case.problem or case.description,
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case.root_cause,
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case.solution,
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similarity,
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)
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)
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+ " |"
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)
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if not result.similar_cases:
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lines.extend(["", "暂无匹配的历史案例，不能据此认定产品无风险。"])
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lines.extend(["", "## 五、标准规则", "", "| 风险点 | 判定规则 | 来源 | 状态 |", "|---|---|---|---|"])
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lines.extend(
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"| "
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+ " | ".join(
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md_text(value)
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for value in (
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rule.risk_point,
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rule.rule,
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rule.source,
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rule.status,
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)
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)
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+ " |"
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for rule in result.standard_judgment
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)
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if not result.standard_judgment:
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lines.extend(["", "暂无匹配标准，需由品控补充核验；不代表已通过合规或检测判定。"])
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lines.extend(["", "## 六、风险预防建议", ""])
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lines.extend(f"{index}. {md_text(value)}" for index, value in enumerate(result.ai_suggestions, 1))
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lines.extend(
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[
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"",
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"## 七、数据口径说明",
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"",
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f"- 数据来自风险平台本地异常案例、采购订单及启用的标准规则；统计区间为 {start} 至 {as_of}。",
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"- 沿用现有评分口径，以截止年份往前 3 年的 1 月 1 日为区间起点。",
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"- 包装组合沿用现有关键词归一化规则，只使用一个匹配物料标签进行评分。",
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"- 缺少巡检、检测结果或可用历史记录的维度按现有默认分处理，并在明细中标注。",
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"- 缺陷维度基于历史 A/B/C 记录计算，不预测新品实际缺陷等级。",
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"- 标准匹配和预防建议不替代实际检测与品控审核；最终放行由业务和品控人员确认。",
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"",
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]
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)
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return "\n".join(lines)
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