OrgTJWater/pipeline-lifetime
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Clarify remaining life export column

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jiang
2026-07-20 15:27:21 +08:00
parent 72b6b63154
commit 1223227ea9
2 changed files with 8 additions and 6 deletions
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app/prediction.py
+3 -2
View File
@@ -56,6 +56,7 @@ FEATURES = [
ID_COLUMN = "管道编号" ID_COLUMN = "管道编号"
PIPE_AGE_COLUMN = "管龄（年）" PIPE_AGE_COLUMN = "管龄（年）"
REMAINING_LIFE_COLUMN = "预计剩余寿命（年）（管道健康状态 ≥0.5 的剩余年数）"
LEGACY_PIPE_AGE_COLUMN = "管龄" LEGACY_PIPE_AGE_COLUMN = "管龄"
COLUMN_ALIASES = { COLUMN_ALIASES = {
"ID": ID_COLUMN, "ID": ID_COLUMN,
@@ -499,7 +500,7 @@ def render_survival_chart(predictions: list[dict[str, Any]], image_path: Path) -
PIPE_AGE_COLUMN: pipe_age, PIPE_AGE_COLUMN: pipe_age,
"当前健康状态": current_health_state, "当前健康状态": current_health_state,
"健康等级": grade_label, "健康等级": grade_label,
"预计剩余寿命（年）": format_years(remaining_life, current_age, max_age), REMAINING_LIFE_COLUMN: format_years(remaining_life, current_age, max_age),
} }
) )
if i < CHART_DISPLAY_LIMIT: if i < CHART_DISPLAY_LIMIT:
@@ -548,7 +549,7 @@ def write_prediction_workbook(
with pd.ExcelWriter(excel_path, engine="openpyxl") as writer: with pd.ExcelWriter(excel_path, engine="openpyxl") as writer:
summary_df = pd.DataFrame( summary_df = pd.DataFrame(
summary_sheet_rows, summary_sheet_rows,
columns=[ID_COLUMN, PIPE_AGE_COLUMN, "当前健康状态", "健康等级", "预计剩余寿命（年）"], columns=[ID_COLUMN, PIPE_AGE_COLUMN, "当前健康状态", "健康等级", REMAINING_LIFE_COLUMN],
) )
sample_df = pd.DataFrame(sample_data_rows, columns=sample_columns) sample_df = pd.DataFrame(sample_data_rows, columns=sample_columns)
if ID_COLUMN in summary_df.columns: if ID_COLUMN in summary_df.columns:
tests/test_prediction.py
+5 -4
View File
@@ -15,6 +15,7 @@ from app.prediction import (
ID_COLUMN, ID_COLUMN,
INPUT_COLUMNS, INPUT_COLUMNS,
PIPE_AGE_COLUMN, PIPE_AGE_COLUMN,
REMAINING_LIFE_COLUMN,
PredictionError, PredictionError,
estimate_remaining_life, estimate_remaining_life,
grade_info, grade_info,
@@ -182,8 +183,8 @@ class PredictionHelpersTest(unittest.TestCase):
] ]
summary_rows = [{"pipe_id": "P001"}, {"pipe_id": "P002"}] summary_rows = [{"pipe_id": "P001"}, {"pipe_id": "P002"}]
summary_sheet_rows = [ summary_sheet_rows = [
{ID_COLUMN: "P001", PIPE_AGE_COLUMN: "10 年", "当前健康状态": 0.7, "健康等级": "IV级", "预计剩余寿命（年）": ">63"}, {ID_COLUMN: "P001", PIPE_AGE_COLUMN: "10 年", "当前健康状态": 0.7, "健康等级": "IV级", REMAINING_LIFE_COLUMN: ">63"},
{ID_COLUMN: "P002", PIPE_AGE_COLUMN: "12 年", "当前健康状态": 0.6, "健康等级": "III级", "预计剩余寿命（年）": ">61"}, {ID_COLUMN: "P002", PIPE_AGE_COLUMN: "12 年", "当前健康状态": 0.6, "健康等级": "III级", REMAINING_LIFE_COLUMN: ">61"},
] ]
with TemporaryDirectory() as temp_dir: with TemporaryDirectory() as temp_dir:
@@ -196,7 +197,7 @@ class PredictionHelpersTest(unittest.TestCase):
summary_data = pd.read_excel(output_path, sheet_name="结果摘要") summary_data = pd.read_excel(output_path, sheet_name="结果摘要")
self.assertEqual( self.assertEqual(
summary_data.columns.tolist(), summary_data.columns.tolist(),
[ID_COLUMN, PIPE_AGE_COLUMN, "当前健康状态", "健康等级", "预计剩余寿命（年）"], [ID_COLUMN, PIPE_AGE_COLUMN, "当前健康状态", "健康等级", REMAINING_LIFE_COLUMN],
) )
sample_data = pd.read_excel(output_path, sheet_name="样本数据") sample_data = pd.read_excel(output_path, sheet_name="样本数据")
@@ -216,7 +217,7 @@ class PredictionHelpersTest(unittest.TestCase):
predictions = [dummy_prediction("00123", [1], [0.9], 10, 0.9)] predictions = [dummy_prediction("00123", [1], [0.9], 10, 0.9)]
summary_rows = [{"pipe_id": "00123"}] summary_rows = [{"pipe_id": "00123"}]
summary_sheet_rows = [ summary_sheet_rows = [
{ID_COLUMN: "00123", PIPE_AGE_COLUMN: "10 年", "当前健康状态": 0.9, "健康等级": "V级", "预计剩余寿命（年）": ">63"} {ID_COLUMN: "00123", PIPE_AGE_COLUMN: "10 年", "当前健康状态": 0.9, "健康等级": "V级", REMAINING_LIFE_COLUMN: ">63"}
] ]
with TemporaryDirectory() as temp_dir: with TemporaryDirectory() as temp_dir: