643 lines
28 KiB
Python
643 lines
28 KiB
Python
"""PDF rendering for the prototype-aligned risk report."""
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from io import BytesIO
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from pathlib import Path
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from typing import Any
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from docx import Document
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from docx.enum.table import WD_CELL_VERTICAL_ALIGNMENT
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from docx.enum.text import WD_ALIGN_PARAGRAPH
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from docx.oxml import OxmlElement
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from docx.shared import Pt, RGBColor
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from reportlab.lib import colors
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from reportlab.lib.enums import TA_CENTER
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from reportlab.lib.pagesizes import A4
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from reportlab.lib.styles import ParagraphStyle, getSampleStyleSheet
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from reportlab.lib.units import mm
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from reportlab.pdfbase import pdfmetrics
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from reportlab.pdfbase.ttfonts import TTFont
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from reportlab.platypus import Paragraph, SimpleDocTemplate, Table, TableStyle
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from app.schemas.risk_prediction import ReportCard, RiskAssessmentResult
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LEVEL_LABELS = {
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"low": "低风险",
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"moderately_low": "较低风险",
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"medium": "中等风险",
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"high": "高风险",
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"very_high": "极高风险",
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}
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def effective_report_cards(result: RiskAssessmentResult) -> list[ReportCard]:
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"""Return the single canonical collection used by every report renderer.
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``ai_solution_cards`` predates the generic card report feature. It is
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accepted as a read-only compatibility input when a client sends an old
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report payload, while new payloads always use ``report_cards``.
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"""
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if result.report_cards:
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return list(result.report_cards)
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return [
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ReportCard(
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type="solution",
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title=item.title,
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confidence=item.confidence,
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analysis_base=item.analysis_base,
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standard_fallback_enabled=item.standard_fallback_enabled,
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standards=item.standards,
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similar_cases=item.similar_cases,
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risks=item.risks,
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suggestions=item.suggestions,
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warning=item.warning,
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)
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for item in result.ai_solution_cards
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]
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def report_card_lines(card: ReportCard) -> list[tuple[str, str]]:
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"""Flatten a card into ordered, renderer-neutral report lines.
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PDF, Word and the web page all consume this same field contract. Keeping
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formatting here prevents one export from silently dropping appended card
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details that are visible in another format.
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"""
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lines: list[tuple[str, str]] = [("卡片类型", card.type)]
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subject = card.name or card.material or card.category
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if subject:
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lines.append(("分析对象", subject))
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if card.party_type:
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lines.append(("对象类型", card.party_type))
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if card.question:
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lines.append(("原始问题", card.question))
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if card.anomaly_count is not None:
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lines.append(("历史异常", f"{card.anomaly_count} 条"))
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if card.cooperation_count is not None:
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lines.append(("合作次数", f"{card.cooperation_count} 次"))
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if card.frequency_coefficient is not None:
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lines.append(("频次系数", f"×{card.frequency_coefficient:.2f}"))
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if card.supplier_base_score is not None:
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lines.append(("供应商基础分", f"{card.supplier_base_score:.2f} 分"))
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if card.supplier_score is not None:
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lines.append(("供应商得分", f"{card.supplier_score:.2f} 分"))
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if card.tier:
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lines.append(("供应商层级", card.tier))
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if card.matched_count is not None:
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lines.append(("匹配数量", f"{card.matched_count} 条"))
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if card.frequency_basis:
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lines.append(("数据口径", card.frequency_basis))
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if card.data_available is not None:
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lines.append(("数据状态", "有匹配数据" if card.data_available else "暂无匹配数据"))
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if card.note:
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lines.append(("备注", card.note))
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if card.detected_values:
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values = "、".join(f"{item.get('name', '')}：{item.get('value', '')}" for item in card.detected_values)
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lines.append(("检测值", values))
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if card.confidence:
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lines.append(("置信度", card.confidence))
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if card.analysis_base:
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lines.append(("分析基础", card.analysis_base))
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if card.standard_fallback_enabled:
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lines.append(("标准兜底校验", "已启用（无直接历史案例）"))
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if card.top_problems:
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lines.append(
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("高频问题", "、".join(f"{item.get('name', '')}（{item.get('count', 0)}次）" for item in card.top_problems))
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)
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if card.frequent_problems:
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lines.append(
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(
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"常见问题",
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"、".join(f"{item.get('name', '')}（{item.get('count', 0)}次）" for item in card.frequent_problems),
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)
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)
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if card.standards:
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lines.append(
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(
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"关联标准",
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"；".join(
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f"{item.category} · {item.risk_point}：{item.rule}"
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f"（{item.source or '标准文件.xlsx'}）· {item.status}"
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for item in card.standards
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),
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)
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)
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if card.similar_cases:
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lines.append(
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(
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"相似案例",
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"；".join(
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f"{item.date or '日期未知'} · {item.product or '未命名产品'} · "
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f"{item.problem or item.description or '异常问题未填写'} → {item.solution or '解决方案未填写'}"
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+ (f"（相似度 {round(item.similarity * 100)}%）" if item.similarity is not None else "")
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for item in card.similar_cases
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),
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)
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)
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if card.risks:
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lines.append(("风险项", "；".join(f"{item.level} · {item.title}：{item.detail}" for item in card.risks)))
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if card.suggestions:
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lines.append(("建议方案", "；".join(card.suggestions)))
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if card.warning:
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lines.append(("风险提示", card.warning))
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return lines
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def _unique_by(items: list[Any], key) -> list[Any]: # noqa: ANN001
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seen: set[Any] = set()
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result: list[Any] = []
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for item in items:
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value = key(item)
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if value in seen:
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continue
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seen.add(value)
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result.append(item)
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return result
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def aggregate_report_data(result: RiskAssessmentResult) -> dict[str, Any]:
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"""Merge appended cards into the report's native sections.
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A card is an input action, not a report section. Its fields are folded
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into the matching section so the browser, PDF and Word output have the
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same information architecture.
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"""
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cards = effective_report_cards(result)
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supplier_cards = [card for card in cards if card.type == "supplier"]
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material_cards = [card for card in cards if card.type == "material"]
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solution_cards = [card for card in cards if card.type == "solution"]
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standard_cards = [card for card in cards if card.type in {"standard", "compliance"}]
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standards = _unique_by(
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[*result.standard_judgment, *(item for card in cards for item in card.standards)],
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lambda item: (item.category, item.risk_point, item.rule, item.status),
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)
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similar_cases = _unique_by(
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[*result.similar_cases, *(item for card in cards for item in card.similar_cases)],
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lambda item: (
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item.case_id,
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item.date,
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item.product,
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item.problem or item.description,
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item.solution,
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),
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)
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# Only base assessment suggestions belong to the prevention section.
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# Card-specific suggestions stay with their owning section (supplier,
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# material, standard/compliance or AI reasoning) to avoid duplication.
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suggestions = _unique_by(list(result.ai_suggestions), lambda item: item)
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detected_values = [item for card in cards for item in card.detected_values]
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risks = [(card.title, risk) for card in solution_cards for risk in card.risks]
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analyses = [card for card in solution_cards if card.analysis_base or card.confidence or card.warning]
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return {
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"cards": cards,
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"supplier_cards": supplier_cards,
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"material_cards": material_cards,
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"solution_cards": solution_cards,
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"standard_cards": standard_cards,
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"standards": standards,
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"similar_cases": similar_cases,
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"suggestions": suggestions,
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"detected_values": detected_values,
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"risks": risks,
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"analyses": analyses,
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}
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def _font_name() -> str:
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candidates = [
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Path("C:/Windows/Fonts/msyh.ttc"),
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Path("C:/Windows/Fonts/msyh.ttf"),
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Path("C:/Windows/Fonts/simhei.ttf"),
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]
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for path in candidates:
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if path.exists():
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name = "DieyaCjk"
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pdfmetrics.registerFont(TTFont(name, str(path), subfontIndex=0))
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return name
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return "Helvetica"
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def build_risk_report_pdf(result: RiskAssessmentResult) -> bytes:
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font = _font_name()
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styles = getSampleStyleSheet()
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title = ParagraphStyle(
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"title", parent=styles["Title"], fontName=font, fontSize=20, alignment=TA_CENTER, spaceAfter=4
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)
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subtitle = ParagraphStyle(
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"subtitle",
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parent=styles["Normal"],
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fontName=font,
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fontSize=9,
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textColor=colors.HexColor("#64748b"),
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alignment=TA_CENTER,
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spaceAfter=14,
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)
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section = ParagraphStyle(
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"section",
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parent=styles["Heading2"],
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fontName=font,
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fontSize=11,
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textColor=colors.HexColor("#111827"),
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leftIndent=5,
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borderPadding=3,
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spaceBefore=12,
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spaceAfter=7,
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)
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body = ParagraphStyle("body", parent=styles["BodyText"], fontName=font, fontSize=9, leading=15)
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small = ParagraphStyle("small", parent=body, fontSize=8, textColor=colors.HexColor("#64748b"))
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supplier_name = result.supplier_freq.supplier_name if result.supplier_freq else None
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report_number = f"RP-{result.subject_id[:8]}-{round(result.score)}"
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generated_at = __import__("datetime").datetime.now().strftime("%Y-%m-%d %H:%M")
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report_data = aggregate_report_data(result)
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standards = report_data["standards"]
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similar_cases = report_data["similar_cases"]
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suggestions = report_data["suggestions"]
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material_cards = report_data["material_cards"]
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standard_cards = report_data["standard_cards"]
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section_shift = 1 if material_cards else 0
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story = [
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Paragraph("新品风险预估报告", title),
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Paragraph(f"蝶雅产品部 · 风险预估系统 · 自动生成 · {generated_at}", subtitle),
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Paragraph(
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f"产品/评估对象：{result.subject_id} 供应商：{supplier_name or '未指定'} 报告编号：{report_number}",
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body,
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),
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]
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def table(rows: list[list[str]], widths: list[float] | None = None) -> Table:
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converted = [[Paragraph(str(cell), body) for cell in row] for row in rows]
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item = Table(converted, colWidths=widths, repeatRows=1)
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item.setStyle(
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TableStyle(
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[
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("BACKGROUND", (0, 0), (-1, 0), colors.HexColor("#111827")),
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("TEXTCOLOR", (0, 0), (-1, 0), colors.white),
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("FONTNAME", (0, 0), (-1, -1), font),
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("GRID", (0, 0), (-1, -1), 0.45, colors.HexColor("#dbe3ef")),
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("VALIGN", (0, 0), (-1, -1), "TOP"),
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("LEFTPADDING", (0, 0), (-1, -1), 7),
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("RIGHTPADDING", (0, 0), (-1, -1), 7),
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("TOPPADDING", (0, 0), (-1, -1), 6),
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("BOTTOMPADDING", (0, 0), (-1, -1), 6),
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]
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)
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)
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return item
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story += [Paragraph("一、风险结论", section)]
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rows = [
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["评估项", "结论"],
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[
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"综合评分",
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f"{round(result.score)} / 100 · "
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f"{LEVEL_LABELS.get(result.level.value, result.level.value)} · "
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f"{result.risk_badge or '需关注'}",
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],
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["风险警示", result.alert_text or "当前暂无足够历史数据，建议按标准库完成基础验证。"],
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]
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dimensions = result.dimension_scores or [
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{"dimension": factor.name, "score": 100 - factor.value, "weight": factor.weight, "reason": None}
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for factor in result.factors
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]
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for item in dimensions:
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name = item.dimension if hasattr(item, "dimension") else item["dimension"]
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if name in {"节点阶段", "缺陷等级"}:
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continue
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score = item.score if hasattr(item, "score") else item["score"]
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weight = item.weight if hasattr(item, "weight") else item["weight"]
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reason = item.reason if hasattr(item, "reason") else item.get("reason")
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supplier = result.supplier_freq
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if name == "供应商表现" and supplier:
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order_text = f"合作 {supplier.cooperation_count} 次、" if supplier.cooperation_count is not None else ""
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conclusion = f"{round(score)} 分 · {order_text}异常 {supplier.anomaly_count or 0} 次"
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elif name == "标准符合性" and result.standard_judgment:
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conclusion = f"{round(score)} 分 · 命中 {len(standards)} 条判定标准，待检测项需人工确认"
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else:
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conclusion = f"{round(score)} 分" + (f" · {reason}" if reason else "")
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rows.append([f"{name}（{weight:g}%）", conclusion])
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story += [table(rows, [55 * mm, 105 * mm])]
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if result.category_warning:
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story += [
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Paragraph("二、品类风险预警", section),
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table(
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[
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["品类", "历史异常", "风险等级", "建议"],
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[
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result.category_warning.category,
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f"{result.category_warning.anomaly_count} 条",
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result.category_warning.risk_level,
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result.category_warning.suggestion or "结合相似品类和标准库预判",
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],
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],
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[35 * mm, 30 * mm, 30 * mm, 65 * mm],
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),
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]
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supplier = result.supplier_freq
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supplier_cards = report_data["supplier_cards"]
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if (supplier and supplier.supplier_name) or supplier_cards:
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supplier_rows = [["维度", "数值", "说明"]]
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if supplier and supplier.cooperation_count is not None:
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supplier_rows.append(["合作次数", f"{supplier.cooperation_count} 次", "近三年采购订单去重单据数"])
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if supplier:
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supplier_rows.append(["异常次数", f"{supplier.anomaly_count or 0} 次", "异常案例库统计"])
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if supplier and supplier.alpha is not None:
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supplier_rows.append(
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["频次风险系数", f"×{supplier.alpha:.2f}", "α = 1.0 +（采购订单数 / 100）× 0.3，最高1.5"]
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)
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if supplier and supplier.tier:
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supplier_rows.append(["供应商层级", supplier.tier, supplier.suggestion or ""])
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for card in supplier_cards:
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if card.name:
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supplier_rows.append(["供应商名称", card.name, "供应商画像统计"])
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for label, value in report_card_lines(card):
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if label not in {"卡片类型", "分析对象", "对象类型"}:
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supplier_rows.append([label, value, "追加供应商画像数据"])
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story += [Paragraph("三、供应商合作频次分析", section), table(supplier_rows, [38 * mm, 35 * mm, 87 * mm])]
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if material_cards:
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story += [Paragraph("四、物料风险分析", section)]
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material_rows = [["物料/字段", "分析结果"]]
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for card in material_cards:
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material_rows.append(
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[
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card.material or card.title,
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"；".join(
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f"{label}：{value}"
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for label, value in report_card_lines(card)
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if label not in {"卡片类型", "分析对象"}
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),
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]
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)
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story.append(table(material_rows, [45 * mm, 115 * mm]))
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story += [Paragraph(f"{'五' if section_shift else '四'}、相似案例匹配（{len(similar_cases)} 条）", section)]
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if similar_cases:
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story.append(
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table(
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[["日期", "产品", "问题", "处理方案"]]
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+ [
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[case.date or "未知", case.product or "未命名", case.problem or "未填写", case.solution or "未填写"]
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for case in similar_cases
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],
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[28 * mm, 42 * mm, 48 * mm, 42 * mm],
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)
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)
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else:
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story.append(Paragraph("暂无相关案例，RAG 相似案例检索未返回匹配结果。", small))
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story += [Paragraph(f"{'六' if section_shift else '五'}、预防建议", section)]
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for index, suggestion in enumerate(suggestions or ["优先针对低分维度安排专项验证，并明确责任人与完成节点。"], 1):
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story.append(Paragraph(f"{index}. {suggestion}", body))
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story += [
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Paragraph(f"{'七' if section_shift else '六'}、AI 推理方案（无历史案例场景）", section),
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Paragraph(
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"本次评估已采用历史案例与标准库双引擎结论；若后续未匹配到足够历史案例，将基于相似品类、相似物料生成类比方案，并由标准库进行规则兜底。",
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body,
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),
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]
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for card in report_data["solution_cards"]:
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if card.analysis_base:
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story.append(Paragraph(f"分析基础：{card.analysis_base}", body))
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if card.confidence:
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story.append(Paragraph(f"置信度：{card.confidence}", body))
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for risk in card.risks:
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story.append(Paragraph(f"{risk.level} · {risk.title}：{risk.detail}", body))
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if card.warning:
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story.append(Paragraph(f"风险提示：{card.warning}", body))
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story += [Paragraph(f"{'八' if section_shift else '七'}、标准判定结果", section)]
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standard_rows = [
|
||
[f"{item.category} · {item.risk_point}", item.rule, item.source or "标准文件.xlsx", item.status]
|
||
for item in standards
|
||
]
|
||
for card in standard_cards:
|
||
if card.detected_values:
|
||
standard_rows.append(
|
||
[
|
||
"检测值",
|
||
"、".join(f"{item.get('name', '')}：{item.get('value', '')}" for item in card.detected_values),
|
||
card.title,
|
||
"待确认",
|
||
]
|
||
)
|
||
if card.note:
|
||
standard_rows.append(["补充说明", card.note, card.title, "待确认"])
|
||
if standard_rows:
|
||
story.append(
|
||
table(
|
||
[["判定维度", "判定标准", "标准来源", "当前状态"]]
|
||
+ standard_rows,
|
||
[38 * mm, 55 * mm, 40 * mm, 27 * mm],
|
||
)
|
||
)
|
||
else:
|
||
story.append(Paragraph("暂无相关标准。", small))
|
||
story.append(Paragraph("双引擎融合：历史异常经验引擎提供风险优先级，标准库提供可执行的检测阈值与验证任务。", small))
|
||
|
||
output = BytesIO()
|
||
doc = SimpleDocTemplate(
|
||
output,
|
||
pagesize=A4,
|
||
rightMargin=18 * mm,
|
||
leftMargin=18 * mm,
|
||
topMargin=16 * mm,
|
||
bottomMargin=16 * mm,
|
||
title="蝶雅新品风险预估报告",
|
||
)
|
||
doc.build(story)
|
||
return output.getvalue()
|
||
|
||
|
||
def build_risk_report_docx(result: RiskAssessmentResult) -> bytes:
|
||
document = Document()
|
||
section = document.sections[0]
|
||
section.page_width = 210 * 36000
|
||
section.page_height = 297 * 36000
|
||
section.top_margin = 16 * 36000
|
||
section.bottom_margin = 16 * 36000
|
||
section.left_margin = 18 * 36000
|
||
section.right_margin = 18 * 36000
|
||
normal = document.styles["Normal"]
|
||
normal.font.name = "微软雅黑"
|
||
normal.font.size = Pt(9)
|
||
title = document.add_paragraph()
|
||
title.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
||
run = title.add_run("新品风险预估报告")
|
||
run.bold = True
|
||
run.font.size = Pt(20)
|
||
subtitle = document.add_paragraph()
|
||
subtitle.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
||
subtitle.add_run(f"蝶雅产品部 · 风险预估系统 · 自动生成 · {result.subject_id}").font.size = Pt(9)
|
||
report_data = aggregate_report_data(result)
|
||
standards = report_data["standards"]
|
||
similar_cases = report_data["similar_cases"]
|
||
suggestions = report_data["suggestions"]
|
||
|
||
def heading(text: str) -> None:
|
||
p = document.add_paragraph()
|
||
p.paragraph_format.space_before = Pt(12)
|
||
p.paragraph_format.space_after = Pt(6)
|
||
p.paragraph_format.left_indent = Pt(5)
|
||
r = p.add_run(text)
|
||
r.bold = True
|
||
r.font.size = Pt(11)
|
||
|
||
def add_table(rows: list[list[str]]) -> None:
|
||
table = document.add_table(rows=len(rows), cols=len(rows[0]))
|
||
table.style = "Table Grid"
|
||
table.autofit = True
|
||
for row_index, row in enumerate(rows):
|
||
for col_index, value in enumerate(row):
|
||
cell = table.cell(row_index, col_index)
|
||
cell.vertical_alignment = WD_CELL_VERTICAL_ALIGNMENT.TOP
|
||
cell.text = str(value)
|
||
for paragraph in cell.paragraphs:
|
||
paragraph.paragraph_format.space_after = Pt(0)
|
||
for run in paragraph.runs:
|
||
run.font.name = "微软雅黑"
|
||
run.font.size = Pt(9)
|
||
if row_index == 0:
|
||
run.bold = True
|
||
run.font.color.rgb = RGBColor(255, 255, 255)
|
||
if row_index == 0:
|
||
shading = OxmlElement("w:shd")
|
||
shading.set("{http://schemas.openxmlformats.org/wordprocessingml/2006/main}fill", "111827")
|
||
cell._tc.get_or_add_tcPr().append(shading)
|
||
document.add_paragraph()
|
||
|
||
heading("一、风险结论")
|
||
rows = [
|
||
["评估项", "结论"],
|
||
[
|
||
"综合评分",
|
||
f"{round(result.score)} / 100 · "
|
||
f"{LEVEL_LABELS.get(result.level.value, result.level.value)} · "
|
||
f"{result.risk_badge or '需关注'}",
|
||
],
|
||
["风险警示", result.alert_text or "当前暂无足够历史数据，建议按标准库完成基础验证。"],
|
||
]
|
||
dimensions = result.dimension_scores or [
|
||
{
|
||
"dimension": factor.name,
|
||
"score": 100 - factor.value,
|
||
"weight": factor.weight,
|
||
"reason": None,
|
||
}
|
||
for factor in result.factors
|
||
]
|
||
for item in dimensions:
|
||
name = item.dimension if hasattr(item, "dimension") else item["dimension"]
|
||
if name in {"节点阶段", "缺陷等级"}:
|
||
continue
|
||
score = item.score if hasattr(item, "score") else item["score"]
|
||
weight = item.weight if hasattr(item, "weight") else item["weight"]
|
||
reason = item.reason if hasattr(item, "reason") else item.get("reason")
|
||
supplier = result.supplier_freq
|
||
if name == "供应商表现" and supplier:
|
||
order_text = f"合作 {supplier.cooperation_count} 次、" if supplier.cooperation_count is not None else ""
|
||
conclusion = f"{round(score)} 分 · {order_text}异常 {supplier.anomaly_count or 0} 次"
|
||
elif name == "标准符合性" and result.standard_judgment:
|
||
conclusion = f"{round(score)} 分 · 命中 {len(standards)} 条判定标准，待检测项需人工确认"
|
||
else:
|
||
conclusion = f"{round(score)} 分" + (f" · {reason}" if reason else "")
|
||
rows.append([f"{name}（{weight:g}%）", conclusion])
|
||
add_table(rows)
|
||
if result.category_warning:
|
||
heading("二、品类风险预警")
|
||
add_table(
|
||
[
|
||
["品类", "历史异常", "风险等级", "建议"],
|
||
[
|
||
result.category_warning.category,
|
||
f"{result.category_warning.anomaly_count} 条",
|
||
result.category_warning.risk_level,
|
||
result.category_warning.suggestion or "结合相似品类和标准库预判",
|
||
],
|
||
]
|
||
)
|
||
supplier_cards = report_data["supplier_cards"]
|
||
if (result.supplier_freq and result.supplier_freq.supplier_name) or supplier_cards:
|
||
heading("三、供应商合作频次分析")
|
||
rows = [["维度", "数值", "说明"]]
|
||
if result.supplier_freq and result.supplier_freq.cooperation_count is not None:
|
||
rows.append(["合作次数", f"{result.supplier_freq.cooperation_count} 次", "近三年采购订单去重单据数"])
|
||
if result.supplier_freq:
|
||
rows.append(["异常次数", f"{result.supplier_freq.anomaly_count or 0} 次", "异常案例库统计"])
|
||
if result.supplier_freq and result.supplier_freq.alpha is not None:
|
||
rows.append(
|
||
["频次风险系数", f"×{result.supplier_freq.alpha:.2f}", "α = 1.0 +（采购订单数 / 100）× 0.3，最高1.5"]
|
||
)
|
||
if result.supplier_freq and result.supplier_freq.tier:
|
||
rows.append(["供应商层级", result.supplier_freq.tier, result.supplier_freq.suggestion or ""])
|
||
for card in supplier_cards:
|
||
if card.name:
|
||
rows.append(["供应商名称", card.name, "供应商画像统计"])
|
||
for label, value in report_card_lines(card):
|
||
if label not in {"卡片类型", "分析对象", "对象类型"}:
|
||
rows.append([label, value, "追加供应商画像数据"])
|
||
add_table(rows)
|
||
material_cards = report_data["material_cards"]
|
||
if material_cards:
|
||
heading("四、物料风险分析")
|
||
rows = [["物料/字段", "分析结果"]]
|
||
for card in material_cards:
|
||
rows.append(
|
||
[
|
||
card.material or card.title,
|
||
"；".join(
|
||
f"{label}：{value}"
|
||
for label, value in report_card_lines(card)
|
||
if label not in {"卡片类型", "分析对象"}
|
||
),
|
||
]
|
||
)
|
||
add_table(rows)
|
||
heading(f"五、相似案例匹配（{len(similar_cases)} 条）")
|
||
if similar_cases:
|
||
add_table(
|
||
[["日期", "产品", "问题", "处理方案"]]
|
||
+ [
|
||
[case.date or "未知", case.product or "未命名", case.problem or "未填写", case.solution or "未填写"]
|
||
for case in similar_cases
|
||
]
|
||
)
|
||
else:
|
||
document.add_paragraph("暂无相关案例，RAG 相似案例检索未返回匹配结果。")
|
||
heading("六、预防建议")
|
||
for index, suggestion in enumerate(suggestions or ["优先针对低分维度安排专项验证，并明确责任人与完成节点。"], 1):
|
||
document.add_paragraph(f"{index}. {suggestion}")
|
||
heading("七、AI 推理方案（无历史案例场景）")
|
||
document.add_paragraph(
|
||
"本次评估已采用历史案例与标准库双引擎结论；若后续未匹配到足够历史案例，将基于相似品类、相似物料生成类比方案，并由标准库进行规则兜底。"
|
||
)
|
||
for card in report_data["solution_cards"]:
|
||
if card.analysis_base:
|
||
document.add_paragraph(f"分析基础：{card.analysis_base}")
|
||
if card.confidence:
|
||
document.add_paragraph(f"置信度：{card.confidence}")
|
||
for risk in card.risks:
|
||
document.add_paragraph(f"{risk.level} · {risk.title}：{risk.detail}")
|
||
if card.warning:
|
||
document.add_paragraph(f"风险提示：{card.warning}")
|
||
heading("八、标准判定结果")
|
||
if standards:
|
||
add_table(
|
||
[["判定维度", "判定标准", "标准来源", "当前状态"]]
|
||
+ [
|
||
[f"{item.category} · {item.risk_point}", item.rule, item.source or "标准文件.xlsx", item.status]
|
||
for item in standards
|
||
]
|
||
)
|
||
else:
|
||
document.add_paragraph("暂无相关标准。")
|
||
document.add_paragraph("双引擎融合：历史异常经验引擎提供风险优先级，标准库提供可执行的检测阈值与验证任务。")
|
||
|
||
output = BytesIO()
|
||
document.save(output)
|
||
return output.getvalue()
|