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defect-analysis
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优化：完善 HTML 报告图表摘要

DESKTOP-74CLTRG\Leol 3 روز پیش
والد
7fa0c7f799
کامیت
b38c2f1979
3فایلهای تغییر یافته به همراه75 افزوده شده و 2 حذف شده
نمای یکپارچه نمایش آمار تفاوت ها
  1. 8 0
      app.py
  2. 38 2
      app_utils.py
  3. 29 0
      tests/test_app_utils.py

+ 8 - 0
app.py
مشاهده فایل 

@@ -31,6 +31,7 @@ from app_utils import (
 
     build_diagnostic_dashboard,
 
     build_diagnostic_dashboard,
 
     build_html_report,
 
     build_html_report,
 
     build_ml_factor_insights,
 
     build_ml_factor_insights,
 
+    build_report_chart_summaries,
 
     calculate_kpis,
 
     calculate_kpis,
 
     calculate_spc_metrics,
 
     calculate_spc_metrics,
 
     generate_industry_diagnosis,
 
     generate_industry_diagnosis,
 
@@ -2596,6 +2597,12 @@ if current_config["show_export"]:
 
     # 7. 生成报告图表
 
     # 7. 生成报告图表
 
     daily_for_chart = filtered_df.groupby("day").size().rename("缺陷数").reset_index() if len(filtered_df) >= 2 else None
 
     daily_for_chart = filtered_df.groupby("day").size().rename("缺陷数").reset_index() if len(filtered_df) >= 2 else None
 
     report_charts = generate_report_charts(filtered_df, daily_trend_df=daily_for_chart)
 
     report_charts = generate_report_charts(filtered_df, daily_trend_df=daily_for_chart)
 
+    chart_summaries = build_report_chart_summaries(
 
+        type_counts_r,
 
+        equipment_counts=eq_counts,
 
+        severity_counts=filtered_df["severity"].value_counts() if "severity" in filtered_df.columns else None,
 
+        trend_summary=trend_summary,
 
+    )
 
 
 
     full_report_html = build_html_report(
 
     full_report_html = build_html_report(
 
         generated_at=datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
 
         generated_at=datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
 
@@ -2611,6 +2618,7 @@ if current_config["show_export"]:
 
         anomaly_rows=anomaly_rows,
 
         anomaly_rows=anomaly_rows,
 
         recommendations=recommendations_r,
 
         recommendations=recommendations_r,
 
         charts=report_charts,
 
         charts=report_charts,
 
+        chart_summaries=chart_summaries,
 
     )
 
     )
 
 
 
     col_exp1, col_exp2, col_exp3 = st.columns(3)
 
     col_exp1, col_exp2, col_exp3 = st.columns(3)

+ 38 - 2
app_utils.py
مشاهده فایل 

@@ -421,7 +421,7 @@ def _fig_to_base64(fig, *, dpi=120):
 
 
 
 
 
 def generate_report_charts(filtered_df, *, daily_trend_df=None):
 
 def generate_report_charts(filtered_df, *, daily_trend_df=None):
 
-    """生成报告内嵌的三张核心图表，返回 dict of base64 data URIs。"""
 
+    """生成报告内嵌的四张核心图表，返回 dict of base64 data URIs。"""
 
     charts = {}
 
     charts = {}
 
 
 
     # --- 1. 缺陷类型分布条形图 ---
 
     # --- 1. 缺陷类型分布条形图 ---
 
@@ -491,7 +491,7 @@ def generate_report_charts(filtered_df, *, daily_trend_df=None):
 
         sev_counts = filtered_df["severity"].value_counts()
 
         sev_counts = filtered_df["severity"].value_counts()
 
         if not sev_counts.empty:
 
         if not sev_counts.empty:
 
             fig, ax = plt.subplots(figsize=(4.5, 3.5))
 
             fig, ax = plt.subplots(figsize=(4.5, 3.5))
 
-            sev_colors = {"轻微": "#22c55e", "一般": "#f59e0b", "严重": "#ef4444"}
 
+            sev_colors = {"轻微": "#22c55e", "中等": "#f59e0b", "严重": "#ef4444"}
 
             colors = [sev_colors.get(name, "#94a3b8") for name in sev_counts.index]
 
             colors = [sev_colors.get(name, "#94a3b8") for name in sev_counts.index]
 
             wedges, texts, autotexts = ax.pie(
 
             wedges, texts, autotexts = ax.pie(
 
                 sev_counts.values, labels=sev_counts.index,
 
                 sev_counts.values, labels=sev_counts.index,
 
@@ -516,6 +516,32 @@ def _series_rows(series):
 
     return list(series.items())
 
     return list(series.items())
 
 
 
 
 
+def build_report_chart_summaries(type_counts, equipment_counts=None, severity_counts=None, trend_summary="-"):
 
+    """生成图表旁的结构化摘要，便于报告快速阅读和追溯。"""
 
+    summaries = []
 
+    type_rows = _series_rows(type_counts)
 
+    if type_rows:
 
+        total = max(sum(int(count) for _, count in type_rows), 1)
 
+        name, count = type_rows[0]
 
+        summaries.append(f"TOP1 缺陷类型：{name}，{int(count)} 个，占比 {count / total:.1%}")
 
+
 
+    equipment_rows = _series_rows(equipment_counts)
 
+    if equipment_rows:
 
+        name, count = equipment_rows[0]
 
+        summaries.append(f"最高缺陷设备：{name}，{int(count)} 个缺陷")
 
+
 
+    severity_rows = _series_rows(severity_counts)
 
+    if severity_rows:
 
+        total = max(sum(int(count) for _, count in severity_rows), 1)
 
+        serious_count = int(dict(severity_rows).get("严重", 0))
 
+        summaries.append(f"严重缺陷占比：{serious_count / total:.1%}")
 
+
 
+    if trend_summary and trend_summary != "-":
 
+        summaries.append(str(trend_summary))
 
+
 
+    return summaries
 
+
 
+
 
 def build_html_report(
 
 def build_html_report(
 
     *,
 
     *,
 
     generated_at,
 
     generated_at,
 
@@ -531,11 +557,13 @@ def build_html_report(
 
     anomaly_rows=None,
 
     anomaly_rows=None,
 
     recommendations=None,
 
     recommendations=None,
 
     charts=None,
 
     charts=None,
 
+    chart_summaries=None,
 
 ):
 
 ):
 
     """生成可直接在浏览器打开的自包含综合 HTML 报告。"""
 
     """生成可直接在浏览器打开的自包含综合 HTML 报告。"""
 
     anomaly_rows = anomaly_rows or []
 
     anomaly_rows = anomaly_rows or []
 
     recommendations = recommendations or []
 
     recommendations = recommendations or []
 
     charts = charts or {}
 
     charts = charts or {}
 
+    chart_summaries = chart_summaries or []
 
     type_rows = _series_rows(type_counts)
 
     type_rows = _series_rows(type_counts)
 
     equipment_rows = _series_rows(equipment_counts)
 
     equipment_rows = _series_rows(equipment_counts)
 
     seat_rows = _series_rows(seat_top)
 
     seat_rows = _series_rows(seat_top)
 
@@ -566,6 +594,9 @@ def build_html_report(
 
     recommendation_items = "\n".join(
 
     recommendation_items = "\n".join(
 
         f"<li>{_escape(item)}</li>" for item in recommendations
 
         f"<li>{_escape(item)}</li>" for item in recommendations
 
     ) or "<li>暂无建议</li>"
 
     ) or "<li>暂无建议</li>"
 
+    chart_summary_items = "\n".join(
 
+        f"<li>{_escape(item)}</li>" for item in chart_summaries
 
+    ) or "<li>暂无图表摘要</li>"
 
 
 
     return f"""<!doctype html>
 
     return f"""<!doctype html>
 
 <html lang="zh-CN">
 
 <html lang="zh-CN">
 
@@ -656,6 +687,11 @@ def build_html_report(
 
           <tr><td>严重缺陷</td><td>{int(kpis.get('critical_defects', 0))} 个</td></tr>
 
           <tr><td>严重缺陷</td><td>{int(kpis.get('critical_defects', 0))} 个</td></tr>
 
         </table>
 
         </table>
 
       </div>
 
       </div>
 
+      <div class="card">
 
+        <h2>图表摘要</h2>
 
+        <ul>{chart_summary_items}</ul>
 
+        <p class="note">摘要由导出时的筛选数据自动生成，适合会议或邮件快速阅读。</p>
 
+      </div>
 
       <div class="card">
 
       <div class="card">
 
         <h2>2. 趋势分析</h2>
 
         <h2>2. 趋势分析</h2>
 
         <p>{_escape(trend_summary)}</p>
 
         <p>{_escape(trend_summary)}</p>

+ 29 - 0
tests/test_app_utils.py
مشاهده فایل 

@@ -12,6 +12,7 @@ from app_utils import (
 
     calculate_kpis,
 
     calculate_kpis,
 
     calculate_spc_metrics,
 
     calculate_spc_metrics,
 
     generate_industry_diagnosis,
 
     generate_industry_diagnosis,
 
+    generate_report_charts,
 
     normalize_defect_schema,
 
     normalize_defect_schema,
 
 )
 
 )
 
 
 
@@ -265,12 +266,40 @@ class AppUtilsTest(unittest.TestCase):
 
             trend_summary="缺陷数趋势: 上升",
 
             trend_summary="缺陷数趋势: 上升",
 
             anomaly_rows=[{"equipment": "LAM-A01", "seat": "R1C1", "count": 4}],
 
             anomaly_rows=[{"equipment": "LAM-A01", "seat": "R1C1", "count": 4}],
 
             recommendations=["重点关注气泡"],
 
             recommendations=["重点关注气泡"],
 
+            chart_summaries=["TOP1 缺陷类型：气泡，占比 58.3%"],
 
         )
 
         )
 
 
 
         self.assertIn("<!doctype html>", html.lower())
 
         self.assertIn("<!doctype html>", html.lower())
 
         self.assertIn("缺陷集中性分析综合报告", html)
 
         self.assertIn("缺陷集中性分析综合报告", html)
 
         self.assertIn("气泡&lt;script&gt;", html)
 
         self.assertIn("气泡&lt;script&gt;", html)
 
         self.assertNotIn("气泡<script>", html)
 
         self.assertNotIn("气泡<script>", html)
 
+        self.assertIn("图表摘要", html)
 
+        self.assertIn("TOP1 缺陷类型：气泡，占比 58.3%", html)
 
+
 
+    def test_generate_report_charts_creates_four_offline_images(self):
 
+        df = pd.DataFrame(
 
+            {
 
+                "defect_id": ["D1", "D2", "D3", "D4"],
 
+                "panel_id": ["P1", "P2", "P3", "P4"],
 
+                "batch_id": ["B1"] * 4,
 
+                "equipment_id": ["LAM-A01", "LAM-A01", "LAM-B01", "LAM-B01"],
 
+                "seat_id": ["R1C1", "R1C2", "R2C1", "R2C2"],
 
+                "timestamp": pd.to_datetime(["2026-04-01", "2026-04-02", "2026-04-02", "2026-04-03"]),
 
+                "defect_type": ["气泡", "气泡", "划痕", "异物"],
 
+                "severity": ["轻微", "中等", "严重", "中等"],
 
+                "shift": ["白班"] * 4,
 
+                "day": ["2026-04-01", "2026-04-02", "2026-04-02", "2026-04-03"],
 
+            }
 
+        )
 
+        daily = df.groupby("day").size().rename("缺陷数").reset_index()
 
+
 
+        charts = generate_report_charts(df, daily_trend_df=daily)
 
+
 
+        self.assertEqual(
 
+            {"type_distribution", "daily_trend", "equipment_distribution", "severity_pie"},
 
+            set(charts),
 
+        )
 
+        self.assertTrue(all(value.startswith("data:image/png;base64,") for value in charts.values()))
 
 
 
 
 
 if __name__ == "__main__":
 
 if __name__ == "__main__":