Update prediction results workflow

app/prediction.py

@@ -61,6 +61,16 @@ PIPE_AGE_COLUMN = "管龄（年）"
 LEGACY_PIPE_AGE_COLUMN = "管龄"
 STATUS_COLUMN = "状态"
 EVENT_AGE_COLUMN = "事件/观察管龄（年）"
+COLUMN_ALIASES = {
+    "管径（mm）": "管径",
+    "管径（mm)": "管径",
+    "流速（m/s）": "流速",
+    "压力（MPa)": "压力",
+    "压力（MPa）": "压力",
+    "温度（℃）": "温度",
+    "年均降雨量（mm）": "降雨量",
+    "降雨量（mm）": "降雨量",
+}
 INPUT_COLUMNS = [
     ID_COLUMN,
     PIPE_AGE_COLUMN,
@@ -68,6 +78,7 @@ INPUT_COLUMNS = [
     EVENT_AGE_COLUMN,
     *FEATURES,
 ]
+CHART_DISPLAY_LIMIT = 10
 MATERIAL_COLUMN = "管材"
 MATERIAL_CODE_OPTIONS = [
     (1, "镀锌"),
@@ -103,6 +114,7 @@ MATERIAL_ALIAS_TO_CODE.update(
 )
 SUPPORTED_EXTENSIONS = {".csv", ".xls", ".xlsx"}
 CHINESE_FONT_PROP = None
+DEFECT_GRADE_VALUES = {"无": 0.0, "轻度": 1.0, "中度": 3.0, "严重": 5.0}
 
 
 class PredictionError(Exception):
@@ -119,12 +131,35 @@ class PredictionArtifacts:
     excel_path: Path
     image_path: Path
     image_filename: str
-    importance_filename: str | None
     sample_count: int
     summary_rows: list[dict[str, Any]]
     analysis_text: str
 
 
+class ModelBundleAdapter:
+    """Expose a bundled preprocessor and survival model as one predictor."""
+
+    def __init__(self, preprocessor, model) -> None:
+        self.preprocessor = preprocessor
+        self.model = model
+
+    def _transform(self, frame: pd.DataFrame):
+        values = frame[FEATURES].copy()
+        values[MATERIAL_COLUMN] = values[MATERIAL_COLUMN].map(
+            lambda value: f"M{int(value)}" if pd.notna(value) else value
+        )
+        values["位置"] = values["位置"].map(
+            lambda value: f"L{int(value)}" if pd.notna(value) else value
+        )
+        return self.preprocessor.transform(values.to_numpy(dtype=object)).astype(np.float32)
+
+    def predict_survival_function(self, frame: pd.DataFrame):
+        return self.model.predict_survival_function(self._transform(frame))
+
+    def predict(self, frame: pd.DataFrame):
+        return self.model.predict(self._transform(frame))
+
+
 def configure_matplotlib_fonts():
     for font_path in CHINESE_FONT_FILES:
         path = Path(font_path)
@@ -159,6 +194,10 @@ def load_model(model_path: str):
     if not os.path.exists(model_path):
         raise FileNotFoundError(f"未找到模型文件: {model_path}")
     model = joblib.load(model_path)
+    if isinstance(model, dict):
+        if not {"preprocessor", "model"}.issubset(model):
+            raise ValueError("模型包缺少 preprocessor 或 model。")
+        model = ModelBundleAdapter(model["preprocessor"], model["model"])
     feature_names = getattr(model, "feature_names_in_", None)
     if feature_names is not None and list(feature_names) != FEATURES:
         raise ValueError(
@@ -195,17 +234,25 @@ def read_input_file(path: Path, suffix: str) -> pd.DataFrame:
         if suffix == ".csv":
             df = pd.read_csv(path, dtype={ID_COLUMN: "string"})
         else:
-            df = pd.read_excel(path, dtype={ID_COLUMN: "string"})
+            try:
+                df = pd.read_excel(path, sheet_name="Template", dtype={ID_COLUMN: "string"})
+            except ValueError:
+                df = pd.read_excel(path, dtype={ID_COLUMN: "string"})
+            df["_excel_row"] = df.index + 2
     except Exception as exc:
         logging.exception("文件解析失败: %s", exc)
         raise PredictionError("文件解析失败，请检查编码或表格格式。")
-    return normalize_input_columns(df)
+    df = normalize_input_columns(df)
+    if suffix in {".xls", ".xlsx"}:
+        df = fill_defects_from_detail_sheet(path, df)
+    return df
 
 
 def normalize_input_columns(df: pd.DataFrame) -> pd.DataFrame:
+    aliases = dict(COLUMN_ALIASES)
     if PIPE_AGE_COLUMN not in df.columns and LEGACY_PIPE_AGE_COLUMN in df.columns:
-        return df.rename(columns={LEGACY_PIPE_AGE_COLUMN: PIPE_AGE_COLUMN})
+        aliases[LEGACY_PIPE_AGE_COLUMN] = PIPE_AGE_COLUMN
-    return df
+    return df.rename(columns={name: aliases[name] for name in df.columns if name in aliases})
 
 
 def normalize_id_value(value: Any, fallback: str) -> str:
@@ -216,6 +263,79 @@ def normalize_id_value(value: Any, fallback: str) -> str:
     return str(value)
 
 
+def defect_grade_value(value: Any) -> float | None:
+    if pd.isna(value):
+        return None
+    text = str(value).strip()
+    if text == "":
+        return None
+    if text in DEFECT_GRADE_VALUES:
+        return DEFECT_GRADE_VALUES[text]
+    try:
+        return float(text)
+    except ValueError:
+        return None
+
+
+def weighted_defect_score(values: list[Any], weights: list[float]) -> float | None:
+    scores = [defect_grade_value(value) for value in values]
+    if any(score is None for score in scores):
+        return None
+    return round(sum(score * weight for score, weight in zip(scores, weights)), 3)
+
+
+def numeric_or_none(value: Any) -> float | None:
+    try:
+        if pd.isna(value):
+            return None
+        return float(value)
+    except (TypeError, ValueError):
+        return None
+
+
+def fill_defects_from_detail_sheet(path: Path, df: pd.DataFrame) -> pd.DataFrame:
+    if "结构缺陷" not in df.columns or "功能缺陷" not in df.columns or "_excel_row" not in df.columns:
+        return df
+
+    structure = pd.to_numeric(df["结构缺陷"], errors="coerce")
+    function = pd.to_numeric(df["功能缺陷"], errors="coerce")
+    needs_fill = structure.isna() | function.isna()
+    df["结构缺陷"] = structure
+    df["功能缺陷"] = function
+    if not needs_fill.any():
+        return df
+
+    try:
+        from openpyxl import load_workbook
+
+        workbook = load_workbook(path, data_only=False, read_only=True)
+        defect_sheet = workbook["缺陷计算"]
+    except Exception as exc:
+        logging.info("未能读取缺陷计算工作表，跳过缺陷自动计算: %s", exc)
+        return df
+
+    for index in df.index[needs_fill]:
+        excel_row = int(df.at[index, "_excel_row"])
+        if pd.isna(df.at[index, "结构缺陷"]):
+            value = numeric_or_none(defect_sheet.cell(excel_row, 5).value)
+            if value is None:
+                value = weighted_defect_score(
+                    [defect_sheet.cell(excel_row, column).value for column in (2, 3, 4)],
+                    [0.5, 0.4, 0.1],
+                )
+            df.at[index, "结构缺陷"] = value
+        if pd.isna(df.at[index, "功能缺陷"]):
+            value = numeric_or_none(defect_sheet.cell(excel_row, 10).value)
+            if value is None:
+                value = weighted_defect_score(
+                    [defect_sheet.cell(excel_row, column).value for column in (6, 7, 8, 9)],
+                    [0.5, 0.2, 0.2, 0.1],
+                )
+            df.at[index, "功能缺陷"] = value
+
+    return df
+
+
 def validate_input_frame(df: pd.DataFrame) -> None:
     if df.empty:
         raise PredictionError("上传文件没有可预测的数据。")
@@ -288,99 +408,6 @@ def make_analysis_text(summary_rows: list[dict[str, Any]]) -> str:
     )
 
 
-def compute_feature_importance(model, x_test: pd.DataFrame) -> np.ndarray | None:
-    try:
-        importances = model.feature_importances_
-    except Exception:
-        importances = None
-
-    if importances is not None and len(importances) == len(FEATURES):
-        vals = np.asarray(importances, dtype=float)
-    else:
-        try:
-            baseline = np.asarray(model.predict(x_test), dtype=float)
-        except Exception as exc:
-            logging.exception("特征重要性基线预测失败: %s", exc)
-            return None
-
-        rng = np.random.default_rng(42)
-        vals = np.zeros(len(FEATURES), dtype=float)
-        for j, feat in enumerate(FEATURES):
-            diffs = []
-            for _ in range(5):
-                x_perm = x_test.copy()
-                x_perm[feat] = rng.permutation(x_perm[feat].to_numpy())
-                try:
-                    perm_pred = np.asarray(model.predict(x_perm), dtype=float)
-                except Exception:
-                    perm_pred = baseline
-                diffs.append(float(np.mean(np.abs(perm_pred - baseline))))
-            vals[j] = float(np.mean(diffs)) if diffs else 0.0
-
-    vals = np.clip(vals, a_min=0.0, a_max=None)
-    total = float(vals.sum())
-    if total > 0:
-        vals = vals / total
-    return vals
-
-
-def render_importance_chart(values: np.ndarray, save_path: Path) -> None:
-    from matplotlib.colors import LinearSegmentedColormap
-
-    keep = values >= 5e-4
-    if not bool(np.any(keep)):
-        keep = np.ones_like(values, dtype=bool)
-    kept_feats = [FEATURES[i] for i in range(len(FEATURES)) if keep[i]]
-    kept_vals = values[keep]
-
-    order = np.argsort(kept_vals)
-    sorted_feats = [kept_feats[k] for k in order]
-    sorted_vals = kept_vals[order]
-    n = len(sorted_vals)
-
-    fig, ax = plt.subplots(figsize=(9, max(3.0, 0.62 * n + 1.6)))
-    cmap = LinearSegmentedColormap.from_list("brand", ["#7fb2e6", "#005EB8", "#0c4188"])
-    colors = cmap(np.linspace(0.15, 1.0, n)) if n else None
-    y_pos = np.arange(n)
-    bars = ax.barh(y_pos, sorted_vals, color=colors, height=0.66, edgecolor="white", linewidth=0.8, zorder=3)
-
-    font_kwargs = chinese_font_kwargs()
-    ax.set_xlabel("相对重要性", fontsize=11, color="#475569", **font_kwargs)
-    ax.set_title("模型输入因素重要性排序", fontsize=15, fontweight="bold", color="#0f172a", pad=14, **font_kwargs)
-    ax.set_yticks(y_pos)
-    ax.set_yticklabels(sorted_feats, **font_kwargs)
-    ax.grid(axis="x", color="#e2e8f0", linewidth=1, zorder=0)
-    ax.set_axisbelow(True)
-    for spine in ("top", "right", "left"):
-        ax.spines[spine].set_visible(False)
-    ax.spines["bottom"].set_color("#cbd5e1")
-    ax.tick_params(axis="y", length=0, labelsize=11)
-    ax.tick_params(axis="x", colors="#94a3b8", labelsize=9)
-    if CHINESE_FONT_PROP:
-        for label in [*ax.get_yticklabels(), *ax.get_xticklabels()]:
-            label.set_fontproperties(CHINESE_FONT_PROP)
-
-    max_val = float(sorted_vals.max()) if n else 0.0
-    for bar, value in zip(bars, sorted_vals):
-        ax.text(
-            bar.get_width() + max_val * 0.012,
-            bar.get_y() + bar.get_height() / 2,
-            f"{value:.1%}",
-            va="center",
-            ha="left",
-            fontsize=10,
-            fontweight="bold",
-            color="#1e293b",
-            **font_kwargs,
-        )
-    if max_val > 0:
-        ax.set_xlim(0, max_val * 1.18)
-
-    fig.tight_layout()
-    fig.savefig(save_path, dpi=160, bbox_inches="tight")
-    plt.close(fig)
-
-
 def run_prediction(uploaded: FileStorage, user_id: int, model) -> PredictionArtifacts:
     original_filename = uploaded.filename or ""
     if not original_filename:
@@ -409,15 +436,6 @@ def run_prediction(uploaded: FileStorage, user_id: int, model) -> PredictionArti
         image_path = IMAGE_DIR / image_filename
         summary_rows, summary_sheet_rows = render_survival_chart(df, curves, image_path)
 
-        importance_filename = None
-        try:
-            importance_values = compute_feature_importance(model, x_test)
-            if importance_values is not None:
-                importance_filename = f"importance_{user_id}_{run_id}.png"
-                render_importance_chart(importance_values, IMAGE_DIR / importance_filename)
-        except Exception as exc:
-            logging.exception("生成特征重要性图失败: %s", exc)
-
         safe_stem = Path(saved_filename).stem
         excel_path = user_dir / f"{safe_stem}_pre.xlsx"
         write_prediction_workbook(excel_path, curves, summary_rows, summary_sheet_rows)
@@ -427,7 +445,6 @@ def run_prediction(uploaded: FileStorage, user_id: int, model) -> PredictionArti
             excel_path=excel_path,
             image_path=image_path,
             image_filename=image_filename,
-            importance_filename=importance_filename,
             sample_count=len(summary_rows),
             summary_rows=summary_rows,
             analysis_text=make_analysis_text(summary_rows),
@@ -441,13 +458,17 @@ def render_survival_chart(df: pd.DataFrame, curves, image_path: Path) -> tuple[l
     plt.figure(figsize=(10, 5.6))
     summary_rows: list[dict[str, Any]] = []
     summary_sheet_rows: list[dict[str, Any]] = []
+    display_note = f"说明：图中仅展示前{CHART_DISPLAY_LIMIT}条管道的示例数据，不足{CHART_DISPLAY_LIMIT}条则全部展示；完整结果请下载电子表格。"
 
     for i, curve in enumerate(curves):
         times = [float(x) for x in list(curve.x)]
         probs = [float(y) for y in list(curve.y)]
         pipe_id = normalize_id_value(df.iloc[i][ID_COLUMN], f"Pipe_{i+1:03d}")
-        pipe_age = f"{df.iloc[i][PIPE_AGE_COLUMN]} 年" if PIPE_AGE_COLUMN in df.columns and pd.notna(df.iloc[i][PIPE_AGE_COLUMN]) else "-"
+        raw_pipe_age = df.iloc[i][PIPE_AGE_COLUMN] if PIPE_AGE_COLUMN in df.columns else None
-        health_probability = interpolate_probability(times, probs, 10)
+        pipe_age_value = float(raw_pipe_age) if pd.notna(raw_pipe_age) else 0.0
+        pipe_age = f"{raw_pipe_age} 年" if pd.notna(raw_pipe_age) else "-"
+        health_probability = interpolate_probability(times, probs, pipe_age_value)
+        health_risk = 1.0 - health_probability
         remaining_life = estimate_remaining_life(times, probs)
         grade_label, grade_desc, grade_class = grade_info(health_probability)
 
@@ -456,6 +477,7 @@ def render_survival_chart(df: pd.DataFrame, curves, image_path: Path) -> tuple[l
                 "pipe_id": pipe_id,
                 "pipe_age": pipe_age,
                 "health_probability": health_probability,
+                "health_risk": health_risk,
                 "remaining_life": remaining_life,
                 "grade_label": grade_label,
                 "grade_desc": grade_desc,
@@ -466,23 +488,25 @@ def render_survival_chart(df: pd.DataFrame, curves, image_path: Path) -> tuple[l
             {
                 ID_COLUMN: pipe_id,
                 PIPE_AGE_COLUMN: pipe_age,
-                "健康等级": grade_label,
+                "健康风险值": health_risk,
             }
         )
-        plt.step(times, probs, where="post", linewidth=2, label=pipe_id)
+        if i < CHART_DISPLAY_LIMIT:
+            plt.step(times, probs, where="post", linewidth=2, label=pipe_id)
 
     font_kwargs = chinese_font_kwargs()
     plt.xlabel("管龄（年）", **font_kwargs)
     plt.ylabel("健康风险", **font_kwargs)
-    plt.title("预测分析图", **font_kwargs)
+    plt.title("管道的剩余寿命分析图", **font_kwargs)
+    plt.figtext(0.5, 0.02, display_note, ha="center", fontsize=9, color="#475569", **font_kwargs)
     plt.grid(alpha=0.18)
-    if len(summary_rows) <= 12:
+    if min(len(summary_rows), CHART_DISPLAY_LIMIT) <= 12:
         plt.legend(loc="best", fontsize=8, prop=CHINESE_FONT_PROP)
     ax = plt.gca()
     if CHINESE_FONT_PROP:
         for label in [*ax.get_xticklabels(), *ax.get_yticklabels()]:
             label.set_fontproperties(CHINESE_FONT_PROP)
-    plt.tight_layout()
+    plt.tight_layout(rect=(0, 0.07, 1, 1))
     plt.savefig(image_path, dpi=160, bbox_inches="tight")
     plt.close()
     return summary_rows, summary_sheet_rows
@@ -511,7 +535,7 @@ def write_prediction_workbook(
     ]
 
     with pd.ExcelWriter(excel_path, engine="openpyxl") as writer:
-        summary_df = pd.DataFrame(summary_sheet_rows, columns=[ID_COLUMN, PIPE_AGE_COLUMN, "健康等级"])
+        summary_df = pd.DataFrame(summary_sheet_rows, columns=[ID_COLUMN, PIPE_AGE_COLUMN, "健康风险值"])
         sample_df = pd.DataFrame(sample_data_rows, columns=sample_columns)
         if ID_COLUMN in summary_df.columns:
             summary_df[ID_COLUMN] = summary_df[ID_COLUMN].astype("string")
@@ -520,6 +544,9 @@ def write_prediction_workbook(
         sample_df.to_excel(writer, sheet_name="样本数据", index=False)
 
         sample_worksheet = writer.book["样本数据"]
+        summary_worksheet = writer.book["结果摘要"]
+        for cell in summary_worksheet["C"][1:]:
+            cell.number_format = "0.0%"
         sample_worksheet.freeze_panes = "A2"
         sample_worksheet.auto_filter.ref = sample_worksheet.dimensions
         sample_worksheet.column_dimensions["A"].width = 12

app/routes.py

@@ -130,16 +130,10 @@ def password_reset_users():
 
 def prediction_result_payload(artifacts, record: UploadRecord) -> dict:
     image_url = url_for("static", filename=f"images/{artifacts.image_filename}")
-    importance_url = (
-        url_for("static", filename=f"images/{artifacts.importance_filename}")
-        if artifacts.importance_filename
-        else None
-    )
     return {
         "original_filename": artifacts.original_filename,
         "generated_at": format_app_datetime(record.upload_time),
         "image_url": image_url,
-        "importance_url": importance_url,
         "excel_url": url_for("main.download_file", record_id=record.id, file_type="prediction"),
         "result_url": url_for("main.result_page"),
         "sample_count": int(artifacts.sample_count),
@@ -439,7 +433,6 @@ def predict():
         {
             "message": "预测成功",
             "image_url": last_result["image_url"],
-            "importance_url": last_result["importance_url"],
             "excel_url": last_result["excel_url"],
             "result_url": last_result["result_url"],
             "sample_count": last_result["sample_count"],

requirements.txt

@@ -14,5 +14,5 @@ openpyxl==3.1.5
 XlsxWriter==3.2.9
 xlrd==2.0.2
 
-scikit-learn==1.8.0
+scikit-learn==1.9.0
-scikit-survival==0.27.0
+scikit-survival==0.28.0

static/js/dashboard.js

@@ -15,8 +15,6 @@ const inlineResult = document.getElementById('inlineResult');
 const resultPlaceholder = document.getElementById('resultPlaceholder');
 const resultContent = document.getElementById('resultContent');
 const resultImage = document.getElementById('resultImage');
-const resultImportanceWrap = document.getElementById('resultImportanceWrap');
-const resultImportanceImage = document.getElementById('resultImportanceImage');
 const excelBtn = document.getElementById('excelBtn');
 const resultPageBtn = document.getElementById('resultPageBtn');
 let selectedFile = null;
@@ -69,13 +67,6 @@ function updateSelectedFile(file) {
 
 function renderResult(data) {
   resultImage.src = data.image_url;
-  if (data.importance_url) {
-    resultImportanceImage.src = data.importance_url;
-    resultImportanceWrap.classList.remove('hidden');
-  } else {
-    resultImportanceWrap.classList.add('hidden');
-    resultImportanceImage.removeAttribute('src');
-  }
   excelBtn.href = data.excel_url;
   resultPageBtn.href = data.result_url;
   resultPlaceholder.classList.add('hidden');

templates/home.html

@@ -7,7 +7,7 @@
 <div class="mb-6 flex flex-col gap-4 lg:flex-row lg:items-end lg:justify-between">
   <div>
     <h1 class="text-3xl font-extrabold tracking-tight sm:text-4xl">管道健康状态与剩余寿命评估</h1>
-    <p class="mt-2 max-w-3xl text-sm leading-6 text-textSub">上传标准数据文件，系统将生成生存概率曲线、健康等级摘要、剩余寿命判断和电子表格预测报告。</p>
+    <p class="mt-2 max-w-3xl text-sm leading-6 text-textSub">上传标准数据文件，系统将生成管道的剩余寿命分析图、健康等级摘要、剩余寿命判断和电子表格预测报告。</p>
   </div>
   <a href="{{ url_for('main.download_template') }}" class="ui-btn ui-btn-secondary text-primary">
     <span class="material-symbols-outlined text-lg">download</span>
@@ -59,7 +59,7 @@
         <div class="mt-5 flex min-h-0 flex-1 flex-col items-center justify-center rounded-md border border-dashed border-outline bg-slate-50 px-5 text-center">
           <span class="material-symbols-outlined text-4xl text-primary">monitoring</span>
           <div class="mt-3 text-sm font-bold text-textMain">暂无预测结果</div>
-          <div class="mt-1 text-xs leading-5 text-textSub">完成一次分析后，将生成生存概率阶梯图、摘要和电子表格报告。</div>
+          <div class="mt-1 text-xs leading-5 text-textSub">完成一次分析后，将生成管道的剩余寿命分析图、摘要和电子表格报告。</div>
         </div>
       </div>
 
@@ -81,15 +81,11 @@
           </div>
         </div>
 
-        <div class="min-h-0 flex-1 overflow-y-auto pr-1">
+        <div class="min-h-0 flex-1 overflow-hidden">
-          <div class="grid gap-4">
+          <div class="flex h-full min-h-0 flex-col rounded-md border border-line bg-slate-50 p-3">
-            <div class="rounded-md border border-line bg-slate-50 p-3">
+            <div class="mb-2 shrink-0 text-xs font-bold text-textSub">管道的剩余寿命分析图</div>
-              <div class="mb-2 text-xs font-bold text-textSub">生存概率阶梯图</div>
+            <div class="min-h-0 flex-1 overflow-hidden rounded bg-white">
-              <img id="resultImage" src="" alt="预测图" class="h-auto w-full rounded bg-white object-contain">
+              <img id="resultImage" src="" alt="预测图" class="h-full w-full object-contain">
-            </div>
-            <div id="resultImportanceWrap" class="hidden rounded-md border border-line bg-slate-50 p-3">
-              <div class="mb-2 text-xs font-bold text-textSub">输入因素重要性</div>
-              <img id="resultImportanceImage" src="" alt="重要性排序图" class="h-auto w-full rounded bg-white object-contain">
             </div>
           </div>
         </div>

templates/login.html

@@ -123,7 +123,7 @@
           供水管道健康状态与<br>
           剩余寿命评估系统<br>
         </h1>
-        <p class="mt-6 text-white/75 text-[15px] max-w-[440px] leading-7">上传管网数据，自动生成健康状态评估、剩余寿命预测与输入因素重要性分析。</p>
+        <p class="mt-6 text-white/75 text-[15px] max-w-[440px] leading-7">上传管网数据，自动生成健康状态评估、剩余寿命预测与电子表格报告。</p>
       </div>
       <div class="text-white/50 text-[12px]">© {{ now_year() }} 供水管道健康评估系统</div>
     </section>

templates/result.html

@@ -23,25 +23,21 @@
 </div>
 
 <div class="grid items-stretch gap-6 lg:grid-cols-[minmax(0,1fr)_360px]">
-  <section class="rounded-lg border border-line bg-white p-5 shadow-panel sm:p-6">
+  <section class="flex h-full flex-col rounded-lg border border-line bg-white p-5 shadow-panel sm:p-6">
-    <div class="mb-4">
+    <div class="mb-4 shrink-0">
       <h2 class="text-xl font-extrabold">管道剩余寿命动态评估</h2>
-      <p class="mt-1 text-sm text-textSub">生存概率随时间变化的阶梯曲线。</p>
+      <p class="mt-1 text-sm text-textSub">管道剩余寿命随时间变化的分析曲线。</p>
     </div>
-    <div class="rounded-md border border-line bg-slate-50 p-3">
+    <div class="flex min-h-[360px] flex-1 items-center justify-center rounded-md border border-line bg-slate-50 p-3">
-      <img src="{{ result.image_url }}" alt="生存概率阶梯图" class="h-auto w-full rounded bg-white object-contain">
+      <img src="{{ result.image_url }}" alt="管道的剩余寿命分析图" class="max-h-full w-full rounded bg-white object-contain">
-    </div>
-    <div class="mt-4 rounded-md border border-blue-100 bg-blueSoft p-4 text-sm leading-6 text-textSub">
-      <span class="font-bold text-textMain">分析说明：</span>{{ result.analysis_text }}
     </div>
   </section>
 
-  <aside class="flex flex-col gap-6">
+  <aside class="flex h-full flex-col gap-6">
     <section class="rounded-lg border border-line bg-white p-5 shadow-panel">
       <h2 class="text-lg font-extrabold">报告摘要</h2>
       <dl class="mt-4 space-y-3 text-sm">
         <div class="flex justify-between gap-4"><dt class="text-textSub">样本数量</dt><dd class="font-bold">{{ result.sample_count }}</dd></div>
-        <div class="flex justify-between gap-4"><dt class="text-textSub">显示样本</dt><dd class="font-bold">{{ result.summary_rows|length }}</dd></div>
         <div class="flex justify-between gap-4"><dt class="text-textSub">输出文件</dt><dd class="font-bold">电子表格</dd></div>
       </dl>
       <a href="{{ result.excel_url }}" class="ui-btn ui-btn-secondary mt-5 w-full text-primary">
@@ -51,52 +47,16 @@
     </section>
 
     <section class="flex flex-1 flex-col rounded-lg border border-line bg-white p-5 shadow-panel">
-      <h2 class="text-lg font-extrabold">结果展示</h2>
+      <h2 class="text-lg font-extrabold">健康等级说明</h2>
-      <div class="mt-4 overflow-hidden rounded-md border border-line">
+      <div class="mt-4 grid gap-2 text-sm">
-        <table class="w-full text-sm">
+        <div class="rounded-md border border-red-100 bg-dangerSoft p-3"><div class="font-extrabold text-dangerText">I级 · (0, 0.2]</div><p class="mt-1 text-xs leading-5">风险十分严重，需立刻抢修或更新改造。</p></div>
-          <thead class="bg-slate-50 text-xs font-bold text-textSub">
+        <div class="rounded-md border border-orange-100 bg-orange-50 p-3"><div class="font-extrabold text-orange-600">II级 · (0.2, 0.4]</div><p class="mt-1 text-xs leading-5">风险较为严重，需尽快检修并加频巡检。</p></div>
-            <tr>
+        <div class="rounded-md border border-amber-100 bg-amber-50 p-3"><div class="font-extrabold text-amber-600">III级 · (0.4, 0.6]</div><p class="mt-1 text-xs leading-5">风险较低，安排定期巡检。</p></div>
-              <th class="px-3 py-3 text-left">编号</th>
+        <div class="rounded-md border border-blue-100 bg-blue-50 p-3"><div class="font-extrabold text-blue-600">IV级 · (0.6, 0.8]</div><p class="mt-1 text-xs leading-5">风险较小，维持常规巡视。</p></div>
-              <th class="px-3 py-3 text-left">管龄</th>
+        <div class="rounded-md border border-blue-100 bg-blueSoft p-3"><div class="font-extrabold text-primary">V级 · (0.8, 1]</div><p class="mt-1 text-xs leading-5">管道安全，维持常规巡视。</p></div>
-              <th class="px-3 py-3 text-left">等级</th>
-            </tr>
-          </thead>
-          <tbody>
-            {% for item in result.summary_rows %}
-            <tr class="border-t border-line">
-              <td class="px-3 py-3 font-semibold">{{ item.pipe_id }}</td>
-              <td class="px-3 py-3 text-textSub">{{ item.pipe_age }}</td>
-              <td class="px-3 py-3"><span class="inline-flex rounded-full px-2.5 py-1 text-xs font-bold {{ item.grade_class }}">{{ item.grade_label }}</span></td>
-            </tr>
-            {% endfor %}
-          </tbody>
-        </table>
       </div>
     </section>
   </aside>
-
-  {% if result.importance_url %}
-  <section class="rounded-lg border border-line bg-white p-5 shadow-panel sm:p-6">
-      <div class="mb-4">
-        <h2 class="text-xl font-extrabold">模型输入因素重要性排序</h2>
-        <p class="mt-1 text-sm text-textSub">各输入因素对预测结果的相对影响程度。</p>
-      </div>
-      <div class="rounded-md border border-line bg-slate-50 p-3">
-        <img src="{{ result.importance_url }}" alt="模型输入因素重要性排序图" class="h-auto w-full rounded bg-white object-contain">
-      </div>
-    </section>
-  {% endif %}
-
-  <aside class="flex flex-col rounded-lg border border-line bg-white p-5 shadow-panel lg:col-start-2">
-    <h2 class="text-lg font-extrabold">健康等级说明</h2>
-    <div class="mt-4 grid flex-1 gap-2 text-sm">
-      <div class="rounded-md border border-red-100 bg-dangerSoft p-3"><div class="font-extrabold text-dangerText">I级 · (0, 0.2]</div><p class="mt-1 text-xs leading-5">风险十分严重，需立刻抢修或更新改造。</p></div>
-      <div class="rounded-md border border-orange-100 bg-orange-50 p-3"><div class="font-extrabold text-orange-600">II级 · (0.2, 0.4]</div><p class="mt-1 text-xs leading-5">风险较为严重，需尽快检修并加频巡检。</p></div>
-      <div class="rounded-md border border-amber-100 bg-amber-50 p-3"><div class="font-extrabold text-amber-600">III级 · (0.4, 0.6]</div><p class="mt-1 text-xs leading-5">风险较低，安排定期巡检。</p></div>
-      <div class="rounded-md border border-blue-100 bg-blue-50 p-3"><div class="font-extrabold text-blue-600">IV级 · (0.6, 0.8]</div><p class="mt-1 text-xs leading-5">风险较小，维持常规巡视。</p></div>
-      <div class="rounded-md border border-blue-100 bg-blueSoft p-3"><div class="font-extrabold text-primary">V级 · (0.8, 1]</div><p class="mt-1 text-xs leading-5">管道安全，维持常规巡视。</p></div>
-    </div>
-  </aside>
 </div>
 {% else %}
 <section class="rounded-lg border border-line bg-white p-10 text-center shadow-panel">

tests/test_prediction.py

@@ -7,8 +7,10 @@ from unittest.mock import patch
 
 import pandas as pd
 from openpyxl import load_workbook
+from openpyxl import Workbook
 
 from app.prediction import (
+    CHART_DISPLAY_LIMIT,
     FEATURES,
     ID_COLUMN,
     INPUT_COLUMNS,
@@ -70,7 +72,8 @@ class PredictionHelpersTest(unittest.TestCase):
         worksheet = workbook["Template"]
         headers = [cell.value for cell in next(worksheet.iter_rows(min_row=1, max_row=1))]
 
-        self.assertEqual(headers, INPUT_COLUMNS)
+        self.assertEqual(len(headers), len(INPUT_COLUMNS))
+        self.assertEqual(set(headers), set(INPUT_COLUMNS))
 
     def test_read_input_file_preserves_text_pipe_ids(self) -> None:
         with TemporaryDirectory() as temp_dir:
@@ -81,6 +84,36 @@ class PredictionHelpersTest(unittest.TestCase):
 
             self.assertEqual(df[ID_COLUMN].tolist(), ["00123"])
 
+    def test_read_input_file_calculates_defects_from_template_detail_sheet(self) -> None:
+        with TemporaryDirectory() as temp_dir:
+            output_path = Path(temp_dir) / "input.xlsx"
+            workbook = Workbook()
+            template = workbook.active
+            template.title = "Template"
+            template.append(INPUT_COLUMNS)
+            template.append(["001", 5, 0, 5, 1, 100, 1.2, 0.4, 20, 800, 1, "=缺陷计算!E2", "=缺陷计算!J2"])
+
+            detail = workbook.create_sheet("缺陷计算")
+            detail.append([
+                ID_COLUMN,
+                "泄漏\n权重0.5",
+                "腐蚀\n权重0.4",
+                "管瘤\n权重0.1",
+                "结构性缺陷值",
+                "气囊\n权重0.5",
+                "杂质\n权重0.2",
+                "异物\n权重0.2",
+                "不明连接\n权重0.1",
+                "功能性缺陷值",
+            ])
+            detail.append(["001", "严重", "中度", "轻度", None, "轻度", "无", "中度", "严重", None])
+            workbook.save(output_path)
+
+            df = read_input_file(output_path, ".xlsx")
+
+            self.assertAlmostEqual(float(df.loc[0, "结构缺陷"]), 3.8)
+            self.assertAlmostEqual(float(df.loc[0, "功能缺陷"]), 1.6)
+
     def test_prepare_model_features_maps_material_aliases_to_codes(self) -> None:
         rows = []
         for value in ["镀锌", "2-钢塑", 13]:
@@ -110,8 +143,8 @@ class PredictionHelpersTest(unittest.TestCase):
         ]
         summary_rows = [{"pipe_id": "P001"}, {"pipe_id": "P002"}]
         summary_sheet_rows = [
-            {ID_COLUMN: "P001", PIPE_AGE_COLUMN: "10 年", "健康概率": 0.7, "预计剩余寿命": 2.0, "健康等级": "IV级"},
+            {ID_COLUMN: "P001", PIPE_AGE_COLUMN: "10 年", "健康风险值": 0.3},
-            {ID_COLUMN: "P002", PIPE_AGE_COLUMN: "12 年", "健康概率": 0.6, "预计剩余寿命": 1.5, "健康等级": "III级"},
+            {ID_COLUMN: "P002", PIPE_AGE_COLUMN: "12 年", "健康风险值": 0.4},
         ]
 
         with TemporaryDirectory() as temp_dir:
@@ -122,7 +155,7 @@ class PredictionHelpersTest(unittest.TestCase):
             self.assertEqual(workbook.sheet_names, ["结果摘要", "样本数据"])
 
             summary_data = pd.read_excel(output_path, sheet_name="结果摘要")
-            self.assertEqual(summary_data.columns.tolist(), [ID_COLUMN, PIPE_AGE_COLUMN, "健康等级"])
+            self.assertEqual(summary_data.columns.tolist(), [ID_COLUMN, PIPE_AGE_COLUMN, "健康风险值"])
 
             sample_data = pd.read_excel(output_path, sheet_name="样本数据")
             self.assertEqual(sample_data.columns.tolist(), ["管龄（年）", "P001", "P002"])
@@ -140,7 +173,7 @@ class PredictionHelpersTest(unittest.TestCase):
     def test_prediction_workbook_writes_pipe_ids_as_excel_text(self) -> None:
         curves = [DummyCurve([1], [0.9])]
         summary_rows = [{"pipe_id": "00123"}]
-        summary_sheet_rows = [{ID_COLUMN: "00123", PIPE_AGE_COLUMN: "10 年", "健康等级": "V级"}]
+        summary_sheet_rows = [{ID_COLUMN: "00123", PIPE_AGE_COLUMN: "10 年", "健康风险值": 0.1}]
 
         with TemporaryDirectory() as temp_dir:
             output_path = Path(temp_dir) / "prediction.xlsx"
@@ -161,13 +194,45 @@ class PredictionHelpersTest(unittest.TestCase):
 
         with TemporaryDirectory() as temp_dir:
             output_path = Path(temp_dir) / "chart.png"
-            with patch("app.prediction.plt.xlabel") as xlabel, patch("app.prediction.plt.ylabel") as ylabel:
+            with (
-                render_survival_chart(df, [DummyCurve([1, 2], [0.9, 0.7])], output_path)
+                patch("app.prediction.plt.xlabel") as xlabel,
+                patch("app.prediction.plt.ylabel") as ylabel,
+                patch("app.prediction.plt.figtext") as figtext,
+                patch("app.prediction.plt.title") as title,
+            ):
+                summary_rows, _ = render_survival_chart(df, [DummyCurve([1, 10, 12], [0.9, 0.7, 0.4])], output_path)
+
+        self.assertAlmostEqual(summary_rows[0]["health_probability"], 0.4)
+        self.assertAlmostEqual(summary_rows[0]["health_risk"], 0.6)
+        self.assertEqual(summary_rows[0]["grade_label"], "II级")
 
         xlabel.assert_called_once()
         self.assertEqual(xlabel.call_args.args[0], "管龄（年）")
         ylabel.assert_called_once()
         self.assertEqual(ylabel.call_args.args[0], "健康风险")
+        title.assert_called_once()
+        self.assertEqual(title.call_args.args[0], "管道的剩余寿命分析图")
+        figtext.assert_called_once()
+        self.assertIn(f"前{CHART_DISPLAY_LIMIT}条管道的示例数据", figtext.call_args.args[2])
+
+    def test_survival_chart_only_plots_first_ten_pipes(self) -> None:
+        sample_count = CHART_DISPLAY_LIMIT + 2
+        df = pd.DataFrame(
+            {
+                ID_COLUMN: [f"P{i:03d}" for i in range(sample_count)],
+                PIPE_AGE_COLUMN: [12] * sample_count,
+            }
+        )
+        curves = [DummyCurve([1, 10, 12], [0.9, 0.7, 0.4]) for _ in range(sample_count)]
+
+        with TemporaryDirectory() as temp_dir:
+            output_path = Path(temp_dir) / "chart.png"
+            with patch("app.prediction.plt.step") as step, patch("app.prediction.plt.legend"):
+                summary_rows, summary_sheet_rows = render_survival_chart(df, curves, output_path)
+
+        self.assertEqual(step.call_count, CHART_DISPLAY_LIMIT)
+        self.assertEqual(len(summary_rows), sample_count)
+        self.assertEqual(len(summary_sheet_rows), sample_count)
 
 
 if __name__ == "__main__":