579 lines
21 KiB
Python
579 lines
21 KiB
Python
from __future__ import annotations
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import logging
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import os
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import uuid
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Any
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import joblib
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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from matplotlib import font_manager, rcParams
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from werkzeug.datastructures import FileStorage
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from werkzeug.utils import secure_filename
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from .config import IMAGE_DIR, UPLOAD_DIR
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from .time_utils import utc_now
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CHINESE_FONT_CANDIDATES = [
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"Noto Sans CJK SC",
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"Noto Sans SC",
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"Noto Sans CJK JP",
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"Noto Sans CJK TC",
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"Source Han Sans SC",
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"WenQuanYi Micro Hei",
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"SimHei",
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"Microsoft YaHei",
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"Arial Unicode MS",
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]
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CHINESE_FONT_FILES = [
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"/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc",
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"/usr/share/fonts/truetype/noto/NotoSansSC-Regular.ttf",
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"/usr/share/fonts/truetype/noto/NotoSansCJK-Regular.ttc",
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"/usr/local/share/fonts/NotoSansCJK-Regular.ttc",
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"/mnt/c/Windows/Fonts/NotoSansSC-VF.ttf",
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"/mnt/c/Windows/Fonts/msyh.ttc",
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"/mnt/c/Windows/Fonts/simhei.ttf",
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"/mnt/c/Windows/Fonts/simsun.ttc",
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]
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FEATURES = [
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"管材",
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"管径",
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"流速",
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"压力",
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"温度",
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"降雨量",
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"位置",
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"结构缺陷",
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"功能缺陷",
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]
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ID_COLUMN = "管道编号"
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PIPE_AGE_COLUMN = "管龄（年）"
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LEGACY_PIPE_AGE_COLUMN = "管龄"
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STATUS_COLUMN = "状态"
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EVENT_AGE_COLUMN = "事件/观察管龄（年）"
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COLUMN_ALIASES = {
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"管径（mm）": "管径",
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"管径（mm)": "管径",
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"流速（m/s）": "流速",
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"压力（MPa)": "压力",
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"压力（MPa）": "压力",
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"温度（℃）": "温度",
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"年均降雨量（mm）": "降雨量",
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"降雨量（mm）": "降雨量",
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}
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INPUT_COLUMNS = [
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ID_COLUMN,
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PIPE_AGE_COLUMN,
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STATUS_COLUMN,
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EVENT_AGE_COLUMN,
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*FEATURES,
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]
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CHART_DISPLAY_LIMIT = 10
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MATERIAL_COLUMN = "管材"
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MATERIAL_CODE_OPTIONS = [
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(1, "镀锌"),
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(2, "钢塑"),
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(3, "铝塑"),
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(4, "PPR"),
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(5, "PE"),
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(6, "UPVC"),
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(7, "铸铁"),
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(8, "预应力"),
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(9, "自应力"),
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(10, "玻璃钢夹砂"),
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(11, "钢管"),
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(12, "钢套混凝土管"),
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(13, "球墨铸铁"),
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(14, "其他"),
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]
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MATERIAL_ALIAS_TO_CODE = {
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str(code): code
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for code, _ in MATERIAL_CODE_OPTIONS
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}
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MATERIAL_ALIAS_TO_CODE.update(
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{
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name.casefold(): code
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for code, name in MATERIAL_CODE_OPTIONS
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}
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)
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MATERIAL_ALIAS_TO_CODE.update(
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{
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f"{code}-{name}".casefold(): code
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for code, name in MATERIAL_CODE_OPTIONS
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}
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)
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SUPPORTED_EXTENSIONS = {".csv", ".xls", ".xlsx"}
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CHINESE_FONT_PROP = None
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DEFECT_GRADE_VALUES = {"无": 0.0, "轻度": 1.0, "中度": 3.0, "严重": 5.0}
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class PredictionError(Exception):
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def __init__(self, message: str, status_code: int = 400) -> None:
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super().__init__(message)
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self.message = message
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self.status_code = status_code
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@dataclass
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class PredictionArtifacts:
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original_filename: str
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saved_path: Path
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excel_path: Path
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image_path: Path
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image_filename: str
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sample_count: int
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summary_rows: list[dict[str, Any]]
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analysis_text: str
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class ModelBundleAdapter:
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"""Expose a bundled preprocessor and survival model as one predictor."""
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def __init__(self, preprocessor, model) -> None:
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self.preprocessor = preprocessor
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self.model = model
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def _transform(self, frame: pd.DataFrame):
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values = frame[FEATURES].copy()
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values[MATERIAL_COLUMN] = values[MATERIAL_COLUMN].map(
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lambda value: f"M{int(value)}" if pd.notna(value) else value
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)
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values["位置"] = values["位置"].map(
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lambda value: f"L{int(value)}" if pd.notna(value) else value
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)
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return self.preprocessor.transform(values.to_numpy(dtype=object)).astype(np.float32)
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def predict_survival_function(self, frame: pd.DataFrame):
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return self.model.predict_survival_function(self._transform(frame))
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def predict(self, frame: pd.DataFrame):
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return self.model.predict(self._transform(frame))
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def configure_matplotlib_fonts():
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for font_path in CHINESE_FONT_FILES:
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path = Path(font_path)
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if path.exists():
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font_manager.fontManager.addfont(str(path))
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prop = font_manager.FontProperties(fname=str(path))
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rcParams["font.family"] = [prop.get_name(), "sans-serif"]
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rcParams["font.sans-serif"] = [prop.get_name(), *CHINESE_FONT_CANDIDATES, "DejaVu Sans"]
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rcParams["axes.unicode_minus"] = False
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return prop
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available_fonts = {font.name for font in font_manager.fontManager.ttflist}
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selected_fonts = [font for font in CHINESE_FONT_CANDIDATES if font in available_fonts]
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if selected_fonts:
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rcParams["font.family"] = [selected_fonts[0], "sans-serif"]
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rcParams["font.sans-serif"] = selected_fonts + ["DejaVu Sans"]
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rcParams["axes.unicode_minus"] = False
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if selected_fonts:
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return font_manager.FontProperties(family=selected_fonts[0])
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logging.warning("未找到中文字体，生成的图表中文可能无法显示。")
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return None
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CHINESE_FONT_PROP = configure_matplotlib_fonts()
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def chinese_font_kwargs() -> dict[str, Any]:
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return {"fontproperties": CHINESE_FONT_PROP} if CHINESE_FONT_PROP else {}
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def load_model(model_path: str):
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if not os.path.exists(model_path):
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raise FileNotFoundError(f"未找到模型文件: {model_path}")
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model = joblib.load(model_path)
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if isinstance(model, dict):
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if not {"preprocessor", "model"}.issubset(model):
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raise ValueError("模型包缺少 preprocessor 或 model。")
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model = ModelBundleAdapter(model["preprocessor"], model["model"])
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feature_names = getattr(model, "feature_names_in_", None)
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if feature_names is not None and list(feature_names) != FEATURES:
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raise ValueError(
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"模型输入特征与系统配置不一致: "
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f"model={list(feature_names)}, app={FEATURES}"
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)
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n_features = getattr(model, "n_features_in_", None)
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if n_features is not None and int(n_features) != len(FEATURES):
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raise ValueError(f"模型特征数量不一致: model={n_features}, app={len(FEATURES)}")
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return model
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def safe_unlink(path: Path) -> None:
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try:
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path.unlink(missing_ok=True)
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except OSError as exc:
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logging.warning("删除文件失败 %s: %s", path, exc)
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def secure_upload_name(original_filename: str, run_id: str) -> tuple[str, str]:
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suffix = Path(original_filename).suffix.lower()
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if suffix not in SUPPORTED_EXTENSIONS:
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raise PredictionError("不支持的格式，仅支持逗号分隔值文件或电子表格文件")
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safe_full_name = secure_filename(original_filename)
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safe_stem = Path(safe_full_name).stem if safe_full_name else ""
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if not safe_stem:
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safe_stem = "upload"
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return f"{safe_stem}_{run_id}{suffix}", suffix
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def read_input_file(path: Path, suffix: str) -> pd.DataFrame:
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try:
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if suffix == ".csv":
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df = pd.read_csv(path, dtype={ID_COLUMN: "string"})
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else:
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try:
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df = pd.read_excel(path, sheet_name="Template", dtype={ID_COLUMN: "string"})
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except ValueError:
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df = pd.read_excel(path, dtype={ID_COLUMN: "string"})
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df["_excel_row"] = df.index + 2
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except Exception as exc:
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logging.exception("文件解析失败: %s", exc)
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raise PredictionError("文件解析失败，请检查编码或表格格式。")
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df = normalize_input_columns(df)
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if suffix in {".xls", ".xlsx"}:
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df = fill_defects_from_detail_sheet(path, df)
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return df
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def normalize_input_columns(df: pd.DataFrame) -> pd.DataFrame:
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aliases = dict(COLUMN_ALIASES)
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if PIPE_AGE_COLUMN not in df.columns and LEGACY_PIPE_AGE_COLUMN in df.columns:
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aliases[LEGACY_PIPE_AGE_COLUMN] = PIPE_AGE_COLUMN
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return df.rename(columns={name: aliases[name] for name in df.columns if name in aliases})
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def normalize_id_value(value: Any, fallback: str) -> str:
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if pd.isna(value):
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return fallback
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if isinstance(value, str):
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return value.strip()
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return str(value)
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def defect_grade_value(value: Any) -> float | None:
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if pd.isna(value):
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return None
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text = str(value).strip()
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if text == "":
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return None
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if text in DEFECT_GRADE_VALUES:
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return DEFECT_GRADE_VALUES[text]
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try:
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return float(text)
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except ValueError:
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return None
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def weighted_defect_score(values: list[Any], weights: list[float]) -> float | None:
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scores = [defect_grade_value(value) for value in values]
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if any(score is None for score in scores):
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return None
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return round(sum(score * weight for score, weight in zip(scores, weights)), 3)
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def numeric_or_none(value: Any) -> float | None:
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try:
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if pd.isna(value):
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return None
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return float(value)
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except (TypeError, ValueError):
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return None
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def fill_defects_from_detail_sheet(path: Path, df: pd.DataFrame) -> pd.DataFrame:
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||
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:
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return df
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||
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structure = pd.to_numeric(df["结构缺陷"], errors="coerce")
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function = pd.to_numeric(df["功能缺陷"], errors="coerce")
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needs_fill = structure.isna() | function.isna()
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df["结构缺陷"] = structure
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df["功能缺陷"] = function
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if not needs_fill.any():
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return df
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try:
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from openpyxl import load_workbook
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||
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workbook = load_workbook(path, data_only=False, read_only=True)
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||
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)
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return df
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for index in df.index[needs_fill]:
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excel_row = int(df.at[index, "_excel_row"])
|
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if pd.isna(df.at[index, "结构缺陷"]):
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value = numeric_or_none(defect_sheet.cell(excel_row, 5).value)
|
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if value is None:
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||
value = weighted_defect_score(
|
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[defect_sheet.cell(excel_row, column).value for column in (2, 3, 4)],
|
||
[0.5, 0.4, 0.1],
|
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)
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||
df.at[index, "结构缺陷"] = value
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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(
|
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[defect_sheet.cell(excel_row, column).value for column in (6, 7, 8, 9)],
|
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[0.5, 0.2, 0.2, 0.1],
|
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)
|
||
df.at[index, "功能缺陷"] = value
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||
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||
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。")
|
||
missing = [col for col in INPUT_COLUMNS if col not in df.columns]
|
||
if missing:
|
||
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")
|
||
if probability <= 0.4:
|
||
return ("II级", "管道安全风险较为严重，需尽快安排检修及加频巡检", "bg-orange-50 text-orange-600")
|
||
if probability <= 0.6:
|
||
return ("III级", "管道安全风险较低，需安排定期巡检", "bg-amber-50 text-amber-600")
|
||
if probability <= 0.8:
|
||
return ("IV级", "管道安全风险较小，维持常规巡视", "bg-blue-50 text-blue-600")
|
||
return ("V级", "管道安全，维持常规巡视", "bg-blueSoft text-primary")
|
||
|
||
|
||
def interpolate_probability(times: list[float], probs: list[float], target: float) -> float:
|
||
if not times:
|
||
return 0.0
|
||
if target <= times[0]:
|
||
return float(probs[0])
|
||
for idx in range(1, len(times)):
|
||
if times[idx] >= target:
|
||
return float(probs[idx])
|
||
return float(probs[-1])
|
||
|
||
|
||
def estimate_remaining_life(times: list[float], probs: list[float]) -> float:
|
||
for t, p in zip(times, probs):
|
||
if p <= 0.5:
|
||
return float(t)
|
||
return float(times[-1]) if times else 0.0
|
||
|
||
|
||
def make_analysis_text(summary_rows: list[dict[str, Any]]) -> str:
|
||
if not summary_rows:
|
||
return "当前结果为空，暂无可供解释的样本。"
|
||
worst = min(summary_rows, key=lambda x: x["health_probability"])
|
||
best = max(summary_rows, key=lambda x: x["health_probability"])
|
||
return (
|
||
"阶梯状曲线表示模型对不同管道随时间推移维持在安全健康状态概率的动态预测。"
|
||
f"当前样本中风险最高管道为 {worst['pipe_id']}（{worst['grade_label']}），"
|
||
f"健康概率最高管道为 {best['pipe_id']}（{best['health_probability']:.1%}）。"
|
||
)
|
||
|
||
|
||
def run_prediction(uploaded: FileStorage, user_id: int, model) -> PredictionArtifacts:
|
||
original_filename = uploaded.filename or ""
|
||
if not original_filename:
|
||
raise PredictionError("未选择文件")
|
||
|
||
timestamp = utc_now().strftime("%Y%m%d%H%M%S")
|
||
run_id = f"{timestamp}_{uuid.uuid4().hex[:8]}"
|
||
saved_filename, suffix = secure_upload_name(original_filename, run_id)
|
||
|
||
user_dir = UPLOAD_DIR / f"user_{user_id}"
|
||
user_dir.mkdir(parents=True, exist_ok=True)
|
||
original_path = user_dir / saved_filename
|
||
uploaded.save(original_path)
|
||
|
||
try:
|
||
df = read_input_file(original_path, suffix)
|
||
validate_input_frame(df)
|
||
x_test = prepare_model_features(df)
|
||
try:
|
||
curves = model.predict_survival_function(x_test)
|
||
except Exception as exc:
|
||
logging.exception("预测失败: %s", exc)
|
||
raise PredictionError("模型预测失败，请检查输入字段类型是否正确。", 500)
|
||
|
||
image_filename = f"plot_{user_id}_{run_id}.png"
|
||
image_path = IMAGE_DIR / image_filename
|
||
summary_rows, summary_sheet_rows = render_survival_chart(df, curves, image_path)
|
||
|
||
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)
|
||
return PredictionArtifacts(
|
||
original_filename=original_filename,
|
||
saved_path=original_path,
|
||
excel_path=excel_path,
|
||
image_path=image_path,
|
||
image_filename=image_filename,
|
||
sample_count=len(summary_rows),
|
||
summary_rows=summary_rows,
|
||
analysis_text=make_analysis_text(summary_rows),
|
||
)
|
||
except PredictionError:
|
||
safe_unlink(original_path)
|
||
raise
|
||
|
||
|
||
def render_survival_chart(df: pd.DataFrame, curves, image_path: Path) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]:
|
||
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}")
|
||
raw_pipe_age = df.iloc[i][PIPE_AGE_COLUMN] if PIPE_AGE_COLUMN in df.columns else None
|
||
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)
|
||
|
||
summary_rows.append(
|
||
{
|
||
"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,
|
||
"grade_class": grade_class,
|
||
}
|
||
)
|
||
summary_sheet_rows.append(
|
||
{
|
||
ID_COLUMN: pipe_id,
|
||
PIPE_AGE_COLUMN: pipe_age,
|
||
"健康风险值": health_risk,
|
||
}
|
||
)
|
||
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("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 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(rect=(0, 0.07, 1, 1))
|
||
plt.savefig(image_path, dpi=160, bbox_inches="tight")
|
||
plt.close()
|
||
return summary_rows, summary_sheet_rows
|
||
|
||
|
||
def write_prediction_workbook(
|
||
excel_path: Path,
|
||
curves,
|
||
summary_rows: list[dict[str, Any]],
|
||
summary_sheet_rows: list[dict[str, Any]],
|
||
) -> None:
|
||
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"]
|
||
chart_times.update(times)
|
||
chart_series.append((pipe_id, dict(zip(times, probs))))
|
||
|
||
sorted_times = sorted(chart_times)
|
||
sample_columns = ["管龄（年）", *[pipe_id for pipe_id, _ in chart_series]]
|
||
sample_data_rows = [
|
||
[time, *[series.get(time) for _, series in chart_series]]
|
||
for time in sorted_times
|
||
]
|
||
|
||
with pd.ExcelWriter(excel_path, engine="openpyxl") as writer:
|
||
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")
|
||
|
||
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"]
|
||
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
|
||
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 = "@"
|