feat: update prediction workbook output

app/prediction.py

@@ -58,6 +58,39 @@ FEATURES = [
 
 ID_COLUMN = "管道编号"
 PIPE_AGE_COLUMN = "管龄"
+MATERIAL_COLUMN = "管材"
+MATERIAL_CODE_OPTIONS = [
+    (1, "镀锌"),
+    (2, "钢塑"),
+    (3, "铝塑"),
+    (4, "PPR"),
+    (5, "PE"),
+    (6, "UPVC"),
+    (7, "铸铁"),
+    (8, "预应力"),
+    (9, "自应力"),
+    (10, "玻璃钢夹砂"),
+    (11, "钢管"),
+    (12, "钢套混凝土管"),
+    (13, "球墨铸铁"),
+    (14, "其他"),
+]
+MATERIAL_ALIAS_TO_CODE = {
+    str(code): code
+    for code, _ in MATERIAL_CODE_OPTIONS
+}
+MATERIAL_ALIAS_TO_CODE.update(
+    {
+        name.casefold(): code
+        for code, name in MATERIAL_CODE_OPTIONS
+    }
+)
+MATERIAL_ALIAS_TO_CODE.update(
+    {
+        f"{code}-{name}".casefold(): code
+        for code, name in MATERIAL_CODE_OPTIONS
+    }
+)
 SUPPORTED_EXTENSIONS = {".csv", ".xls", ".xlsx"}
 CHINESE_FONT_PROP = None
 
@@ -150,13 +183,21 @@ def secure_upload_name(original_filename: str, run_id: str) -> tuple[str, str]:
 def read_input_file(path: Path, suffix: str) -> pd.DataFrame:
     try:
         if suffix == ".csv":
-            return pd.read_csv(path)
+            return pd.read_csv(path, dtype={ID_COLUMN: "string"})
-        return pd.read_excel(path)
+        return pd.read_excel(path, dtype={ID_COLUMN: "string"})
     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。")
 
 
+def normalize_id_value(value: Any, fallback: str) -> str:
+    if pd.isna(value):
+        return fallback
+    if isinstance(value, str):
+        return value.strip()
+    return str(value)
+
+
 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。")
@@ -166,6 +207,28 @@ def validate_input_frame(df: pd.DataFrame) -> None:
         raise PredictionError(f"缺少必要字段: {', '.join(missing)}")
 
 
+def normalize_material_code(value: Any) -> int:
+    if pd.isna(value):
+        raise PredictionError("管材不能为空。")
+    if isinstance(value, str):
+        key = value.strip().casefold()
+    else:
+        numeric_value = float(value)
+        if not numeric_value.is_integer():
+            raise PredictionError(f"管材编码无效: {value}")
+        key = str(int(numeric_value))
+    code = MATERIAL_ALIAS_TO_CODE.get(key)
+    if code is None:
+        raise PredictionError(f"管材编码无效: {value}")
+    return code
+
+
+def prepare_model_features(df: pd.DataFrame) -> pd.DataFrame:
+    x_test = df[FEATURES].copy()
+    x_test[MATERIAL_COLUMN] = x_test[MATERIAL_COLUMN].map(normalize_material_code)
+    return x_test
+
+
 def grade_info(probability: float) -> tuple[str, str, str]:
     if probability <= 0.2:
         return ("I级", "管道安全风险十分严重，需立刻进行抢修或更新改造", "bg-dangerSoft text-dangerText")
@@ -318,7 +381,7 @@ def run_prediction(uploaded: FileStorage, user_id: int, model) -> PredictionArti
     try:
         df = read_input_file(original_path, suffix)
         validate_input_frame(df)
-        x_test = df[FEATURES].copy()
+        x_test = prepare_model_features(df)
         try:
             curves = model.predict_survival_function(x_test)
         except Exception as exc:
@@ -365,7 +428,7 @@ def render_survival_chart(df: pd.DataFrame, curves, image_path: Path) -> tuple[l
     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 = str(df.iloc[i][ID_COLUMN]) if pd.notna(df.iloc[i][ID_COLUMN]) else f"Pipe_{i+1:03d}"
+        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 "-"
         health_probability = interpolate_probability(times, probs, 10)
         remaining_life = estimate_remaining_life(times, probs)
@@ -386,15 +449,13 @@ def render_survival_chart(df: pd.DataFrame, curves, image_path: Path) -> tuple[l
             {
                 "管道编号": pipe_id,
                 "管龄": pipe_age,
-                "健康概率": health_probability,
-                "预计剩余寿命": remaining_life,
                 "健康等级": grade_label,
             }
         )
         plt.step(times, probs, where="post", linewidth=2, label=pipe_id)
 
     font_kwargs = chinese_font_kwargs()
-    plt.xlabel("预测时间轴（年）", **font_kwargs)
+    plt.xlabel("管龄（年）", **font_kwargs)
     plt.ylabel("生存概率", **font_kwargs)
     plt.title("预测分析图", **font_kwargs)
     plt.grid(alpha=0.18)
@@ -416,22 +477,58 @@ def write_prediction_workbook(
     summary_rows: list[dict[str, Any]],
     summary_sheet_rows: list[dict[str, Any]],
 ) -> None:
-    sample_data_rows: list[dict[str, Any]] = []
+    chart_times: set[float] = set()
+    chart_series: list[tuple[str, dict[float, float]]] = []
     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 = summary_rows[i]["pipe_id"]
-        for time, probability in zip(times, probs):
+        chart_times.update(times)
-            sample_data_rows.append(
+        chart_series.append((pipe_id, dict(zip(times, probs))))
-                {
+
-                    "管道编号": pipe_id,
+    sorted_times = sorted(chart_times)
-                    "样本序号": i + 1,
+    sample_columns = ["管龄（年）", *[pipe_id for pipe_id, _ in chart_series]]
-                    "时间（年）": time,
+    sample_data_rows = [
-                    "生存概率": probability,
+        [time, *[series.get(time) for _, series in chart_series]]
-                    "风险概率": 1 - probability,
+        for time in sorted_times
-                }
+    ]
-            )
 
     with pd.ExcelWriter(excel_path, engine="openpyxl") as writer:
-        pd.DataFrame(summary_sheet_rows).to_excel(writer, sheet_name="结果摘要", index=False)
+        summary_df = pd.DataFrame(summary_sheet_rows, columns=[ID_COLUMN, PIPE_AGE_COLUMN, "健康等级"])
-        pd.DataFrame(sample_data_rows).to_excel(writer, sheet_name="样本数据", index=False)
+        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")
+
+        summary_df.to_excel(writer, sheet_name="结果摘要", index=False)
+        sample_df.to_excel(writer, sheet_name="样本数据", index=False)
+
+        sample_worksheet = writer.book["样本数据"]
+        sample_worksheet.freeze_panes = "A2"
+        sample_worksheet.auto_filter.ref = sample_worksheet.dimensions
+        sample_worksheet.column_dimensions["A"].width = 12
+        for column_cells in sample_worksheet.iter_cols(min_col=2, max_col=sample_worksheet.max_column):
+            header_cell = column_cells[0]
+            sample_worksheet.column_dimensions[header_cell.column_letter].width = max(12, len(str(header_cell.value)) + 2)
+            for cell in column_cells[1:]:
+                cell.number_format = "0.0%"
+
+        for sheet_name in ("结果摘要", "样本数据"):
+            worksheet = writer.book[sheet_name]
+            header_cells = next(worksheet.iter_rows(min_row=1, max_row=1), [])
+            id_column_index = None
+            for cell in header_cells:
+                if cell.value == ID_COLUMN:
+                    id_column_index = cell.column
+                    break
+            if id_column_index is None:
+                continue
+            for cell in worksheet.iter_cols(
+                min_col=id_column_index,
+                max_col=id_column_index,
+                min_row=2,
+                max_row=worksheet.max_row,
+            ):
+                for item in cell:
+                    if item.value is not None:
+                        item.value = str(item.value)
+                    item.number_format = "@"

app/routes.py

@@ -30,6 +30,7 @@ from .time_utils import format_datetime_for_timezone
 
 bp = Blueprint("main", __name__)
 REFERENCE_PDF_NAME = "20260630标准文本——供水管道健康状态与剩余寿命评估技术导则.pdf"
+TEMPLATE_EXCEL_NAME = "管道预测数据模板.xlsx"
 REGISTRATION_SETTING_KEY = "allow_registration"
 RECORDS_PER_PAGE = 10
 
@@ -375,7 +376,7 @@ def download_template():
     template_path = BASE_DIR / "example.xlsx"
     if not template_path.exists():
         abort(404)
-    return send_file(template_path, as_attachment=True, download_name="example.xlsx")
+    return send_file(template_path, as_attachment=True, download_name=TEMPLATE_EXCEL_NAME)
 
 
 @bp.route("/reference_pdf")

static/js/dashboard.js

@@ -19,7 +19,6 @@ const resultImportanceWrap = document.getElementById('resultImportanceWrap');
 const resultImportanceImage = document.getElementById('resultImportanceImage');
 const excelBtn = document.getElementById('excelBtn');
 const resultPageBtn = document.getElementById('resultPageBtn');
-const homeStateKey = 'pipelineLifetime.homeState';
 let selectedFile = null;
 let alertTimer = null;
 let alertHideTimer = null;
@@ -66,24 +65,6 @@ function updateSelectedFile(file) {
   selectedFile = file;
   selectedFileName.textContent = '已选择文件：' + file.name;
   selectedFileName.classList.remove('hidden');
-  saveHomeState({ selectedFilename: file.name });
-}
-
-function readHomeState() {
-  try {
-    return JSON.parse(sessionStorage.getItem(homeStateKey)) || {};
-  } catch (err) {
-    return {};
-  }
-}
-
-function saveHomeState(nextState) {
-  try {
-    const currentState = readHomeState();
-    sessionStorage.setItem(homeStateKey, JSON.stringify({ ...currentState, ...nextState }));
-  } catch (err) {
-    // Ignore storage failures; prediction still works without client-side restore.
-  }
 }
 
 function renderResult(data) {
@@ -102,17 +83,6 @@ function renderResult(data) {
   resultContent.classList.add('flex');
 }
 
-function restoreHomeState() {
-  const savedState = readHomeState();
-  if (savedState.selectedFilename) {
-    selectedFileName.textContent = '上次选择文件：' + savedState.selectedFilename + '（需重新选择）';
-    selectedFileName.classList.remove('hidden');
-  }
-  if (savedState.result) {
-    renderResult(savedState.result);
-  }
-}
-
 fileInput.addEventListener('change', () => {
   const file = fileInput.files[0];
   if (!file) return;
@@ -176,15 +146,6 @@ form.addEventListener('submit', async (e) => {
       return;
     }
     renderResult(data);
-    saveHomeState({
-      selectedFilename: data.original_filename || selectedFile.name,
-      result: {
-        image_url: data.image_url,
-        importance_url: data.importance_url || '',
-        excel_url: data.excel_url,
-        result_url: data.result_url
-      }
-    });
     showAlert('预测完成，已生成图表与电子表格报告。', 'success');
     inlineResult.scrollIntoView({ behavior: 'smooth', block: 'nearest' });
   } catch (err) {
@@ -197,5 +158,3 @@ form.addEventListener('submit', async (e) => {
     submitIcon = restoredIcon;
   }
 });
-
-restoreHomeState();

templates/base.html

@@ -142,15 +142,6 @@
         showAppNotification(message, category === 'error' ? 'error' : 'info');
       }
 
-      document.querySelectorAll('[data-clear-home-state]').forEach((form) => {
-        form.addEventListener('submit', () => {
-          try {
-            sessionStorage.removeItem('pipelineLifetime.homeState');
-          } catch (err) {
-            // Ignore storage failures during logout.
-          }
-        });
-      });
     })();
   </script>
   {% block scripts %}{% endblock %}

templates/result.html

@@ -51,12 +51,13 @@
     </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">样本预览</h2>
+      <h2 class="text-lg font-extrabold">结果展示</h2>
       <div class="mt-4 overflow-hidden rounded-md border border-line">
         <table class="w-full text-sm">
           <thead class="bg-slate-50 text-xs font-bold text-textSub">
             <tr>
-              <th class="px-3 py-3 text-left">管道编号</th>
+              <th class="px-3 py-3 text-left">编号</th>
+              <th class="px-3 py-3 text-left">管龄</th>
               <th class="px-3 py-3 text-left">等级</th>
             </tr>
           </thead>
@@ -64,6 +65,7 @@
             {% 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 %}

tests/test_auth_registration.py

@@ -130,6 +130,19 @@ class RegistrationRoutesTest(unittest.TestCase):
             self.assertIn('name="captcha"', html)
             self.assertIn(captcha, html)
 
+    def test_template_download_uses_chinese_filename(self) -> None:
+        with TemporaryDirectory() as temp_dir:
+            app = self.create_test_app(temp_dir, allow_registration=True)
+
+            response = app.test_client().get("/download_template")
+
+            self.assertEqual(response.status_code, 200)
+            self.assertIn("attachment", response.headers["Content-Disposition"])
+            self.assertIn(
+                "filename*=UTF-8''%E7%AE%A1%E9%81%93%E9%A2%84%E6%B5%8B%E6%95%B0%E6%8D%AE%E6%A8%A1%E6%9D%BF.xlsx",
+                response.headers["Content-Disposition"],
+            )
+
     def test_register_post_does_not_create_user_when_registration_is_closed(self) -> None:
         with TemporaryDirectory() as temp_dir:
             app = self.create_test_app(temp_dir, allow_registration=False)

tests/test_prediction.py

@@ -3,16 +3,22 @@ from __future__ import annotations
 import unittest
 from pathlib import Path
 from tempfile import TemporaryDirectory
+from unittest.mock import patch
 
 import pandas as pd
+from openpyxl import load_workbook
 
 from app.prediction import (
     FEATURES,
     ID_COLUMN,
+    PIPE_AGE_COLUMN,
     PredictionError,
     estimate_remaining_life,
     grade_info,
     interpolate_probability,
+    prepare_model_features,
+    read_input_file,
+    render_survival_chart,
     secure_upload_name,
     validate_input_frame,
     write_prediction_workbook,
@@ -53,6 +59,37 @@ class PredictionHelpersTest(unittest.TestCase):
 
         self.assertIn("缺少必要字段", ctx.exception.message)
 
+    def test_read_input_file_preserves_text_pipe_ids(self) -> None:
+        with TemporaryDirectory() as temp_dir:
+            output_path = Path(temp_dir) / "input.xlsx"
+            pd.DataFrame({ID_COLUMN: ["00123"], FEATURES[0]: [1]}).to_excel(output_path, index=False)
+
+            df = read_input_file(output_path, ".xlsx")
+
+            self.assertEqual(df[ID_COLUMN].tolist(), ["00123"])
+
+    def test_prepare_model_features_maps_material_aliases_to_codes(self) -> None:
+        rows = []
+        for value in ["镀锌", "2-钢塑", 13]:
+            row = {feature: 1 for feature in FEATURES}
+            row["管材"] = value
+            rows.append(row)
+        df = pd.DataFrame(rows)
+
+        x_test = prepare_model_features(df)
+
+        self.assertEqual(x_test["管材"].tolist(), [1, 2, 13])
+
+    def test_prepare_model_features_rejects_invalid_material_alias(self) -> None:
+        row = {feature: 1 for feature in FEATURES}
+        row["管材"] = "未知管材"
+        df = pd.DataFrame([row])
+
+        with self.assertRaises(PredictionError) as ctx:
+            prepare_model_features(df)
+
+        self.assertIn("管材编码无效", ctx.exception.message)
+
     def test_prediction_workbook_keeps_sample_data_in_one_sheet(self) -> None:
         curves = [
             DummyCurve([1, 2], [0.9, 0.7]),
@@ -60,8 +97,8 @@ class PredictionHelpersTest(unittest.TestCase):
         ]
         summary_rows = [{"pipe_id": "P001"}, {"pipe_id": "P002"}]
         summary_sheet_rows = [
-            {"管道编号": "P001", "健康概率": 0.7},
+            {"管道编号": "P001", "管龄": "10 年", "健康概率": 0.7, "预计剩余寿命": 2.0, "健康等级": "IV级"},
-            {"管道编号": "P002", "健康概率": 0.6},
+            {"管道编号": "P002", "管龄": "12 年", "健康概率": 0.6, "预计剩余寿命": 1.5, "健康等级": "III级"},
         ]
 
         with TemporaryDirectory() as temp_dir:
@@ -71,10 +108,51 @@ class PredictionHelpersTest(unittest.TestCase):
             workbook = pd.ExcelFile(output_path)
             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"])
+
             sample_data = pd.read_excel(output_path, sheet_name="样本数据")
-            self.assertEqual(len(sample_data), 4)
+            self.assertEqual(sample_data.columns.tolist(), ["管龄（年）", "P001", "P002"])
-            self.assertEqual(sample_data["管道编号"].tolist(), ["P001", "P001", "P002", "P002"])
+            self.assertEqual(len(sample_data), 2)
-            self.assertIn("风险概率", sample_data.columns)
+            self.assertEqual(sample_data["管龄（年）"].tolist(), [1, 2])
+            self.assertEqual(sample_data["P001"].tolist(), [0.9, 0.7])
+            self.assertEqual(sample_data["P002"].tolist(), [0.8, 0.6])
+
+            openpyxl_workbook = load_workbook(output_path)
+            sample_worksheet = openpyxl_workbook["样本数据"]
+            self.assertEqual(sample_worksheet.freeze_panes, "A2")
+            self.assertEqual(sample_worksheet.auto_filter.ref, "A1:C3")
+            self.assertEqual(sample_worksheet["B2"].number_format, "0.0%")
+
+    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"}]
+
+        with TemporaryDirectory() as temp_dir:
+            output_path = Path(temp_dir) / "prediction.xlsx"
+            write_prediction_workbook(output_path, curves, summary_rows, summary_sheet_rows)
+
+            workbook = load_workbook(output_path)
+            summary_worksheet = workbook["结果摘要"]
+            self.assertEqual(summary_worksheet["A2"].value, "00123")
+            self.assertEqual(summary_worksheet["A2"].data_type, "s")
+            self.assertEqual(summary_worksheet["A2"].number_format, "@")
+
+            sample_worksheet = workbook["样本数据"]
+            self.assertEqual(sample_worksheet["B1"].value, "00123")
+            self.assertEqual(sample_worksheet["B1"].data_type, "s")
+
+    def test_survival_chart_uses_pipe_age_x_axis_label(self) -> None:
+        df = pd.DataFrame({ID_COLUMN: ["P001"], PIPE_AGE_COLUMN: [12]})
+
+        with TemporaryDirectory() as temp_dir:
+            output_path = Path(temp_dir) / "chart.png"
+            with patch("app.prediction.plt.xlabel") as xlabel:
+                render_survival_chart(df, [DummyCurve([1, 2], [0.9, 0.7])], output_path)
+
+        xlabel.assert_called_once()
+        self.assertEqual(xlabel.call_args.args[0], "管龄（年）")
 
 
 if __name__ == "__main__":