# -*- coding: utf-8 -*- """ 运行要求： 1. 同目录放置 my_survival_forest_model_quxi-10-0331.joblib 2. pip install flask flask_sqlalchemy flask_login pandas joblib matplotlib openpyxl xlsxwriter xlrd 3. python final_flask_app_0331_strict.py """ from __future__ import annotations import logging import os import random import sys from dataclasses import dataclass from datetime import datetime from io import BytesIO from pathlib import Path from typing import Any, Dict, List import joblib import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt import numpy as np import pandas as pd from flask import ( Flask, abort, flash, jsonify, redirect, render_template_string, request, send_file, session, url_for, ) from flask_login import ( LoginManager, UserMixin, current_user, login_required, login_user, logout_user, ) from flask_sqlalchemy import SQLAlchemy from matplotlib import font_manager, rcParams from werkzeug.security import check_password_hash, generate_password_hash from werkzeug.utils import secure_filename CHINESE_FONT_CANDIDATES = [ "Noto Sans CJK SC", "Noto Sans CJK JP", "Noto Sans CJK TC", "Source Han Sans SC", "WenQuanYi Micro Hei", "SimHei", "Microsoft YaHei", "Arial Unicode MS", ] def configure_matplotlib_fonts() -> None: available_fonts = {font.name for font in font_manager.fontManager.ttflist} selected_fonts = [font for font in CHINESE_FONT_CANDIDATES if font in available_fonts] rcParams["font.sans-serif"] = selected_fonts + ["DejaVu Sans"] configure_matplotlib_fonts() rcParams["axes.unicode_minus"] = False BASE_DIR = Path(__file__).resolve().parent DATA_DIR = BASE_DIR / "data" STATIC_DIR = BASE_DIR / "static" UPLOAD_DIR = BASE_DIR / "uploads" IMAGE_DIR = STATIC_DIR / "images" FEATURES = [ "管材", "管径", "流速", "压力", "温度", "降雨量", "位置", "结构缺陷", "功能缺陷", ] ID_COLUMN = "管道编号" TEMPLATE_COLUMNS = [ ID_COLUMN, "管龄", "状态", "管材", "管径", "流速", "压力", "温度", "降雨量", "位置", "结构缺陷", "功能缺陷", ] DATA_DIR.mkdir(parents=True, exist_ok=True) logging.basicConfig( filename=str(DATA_DIR / "app.log"), level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s", ) app = Flask(__name__) app.config["SECRET_KEY"] = "pipe-survival-0331-strict-secret" app.config["SQLALCHEMY_DATABASE_URI"] = f"sqlite:///{DATA_DIR / 'pipe_survival_0331.db'}" app.config["SQLALCHEMY_TRACK_MODIFICATIONS"] = False app.config["UPLOAD_FOLDER"] = str(UPLOAD_DIR) db = SQLAlchemy(app) login_manager = LoginManager(app) login_manager.login_view = "login" login_manager.login_message = "请先登录后再访问该页面。" class User(UserMixin, db.Model): __tablename__ = "users" id = db.Column(db.Integer, primary_key=True) username = db.Column(db.String(100), unique=True, nullable=False) password_hash = db.Column(db.String(255), nullable=False) is_admin = db.Column(db.Boolean, default=False, nullable=False) created_at = db.Column(db.DateTime, default=datetime.utcnow) def set_password(self, password: str) -> None: self.password_hash = generate_password_hash(password) def check_password(self, password: str) -> bool: return check_password_hash(self.password_hash, password) class UploadRecord(db.Model): __tablename__ = "upload_records" id = db.Column(db.Integer, primary_key=True) user_id = db.Column(db.Integer, db.ForeignKey("users.id"), nullable=False) original_filename = db.Column(db.String(255), nullable=False) saved_path = db.Column(db.String(500), nullable=False) prediction_path = db.Column(db.String(500), nullable=False) image_path = db.Column(db.String(500), nullable=False) upload_time = db.Column(db.DateTime, default=datetime.utcnow) user = db.relationship("User", backref=db.backref("uploads", lazy=True)) @login_manager.user_loader def load_user(user_id: str): return db.session.get(User, int(user_id)) @dataclass class PipeSummary: pipe_id: str pipe_age: str grade_label: str def resource_path(relative_path: str) -> str: try: base_path = Path(sys._MEIPASS) # type: ignore[attr-defined] except Exception: base_path = BASE_DIR return str((base_path / relative_path).resolve()) def ensure_dirs() -> None: for path in [DATA_DIR, STATIC_DIR, IMAGE_DIR, UPLOAD_DIR]: path.mkdir(parents=True, exist_ok=True) def init_app() -> None: ensure_dirs() with app.app_context(): db.create_all() if not User.query.filter_by(username="admin").first(): admin = User(username="admin", is_admin=True) admin.set_password("admin123") db.session.add(admin) db.session.commit() try: app.config["RSF_MODEL"] = load_model() logging.info("模型加载成功") except Exception as exc: app.config["RSF_MODEL"] = None logging.exception("模型加载失败: %s", exc) def load_model(): model_path = resource_path("my_survival_forest_model_quxi-10-0331.joblib") if not os.path.exists(model_path): raise FileNotFoundError(f"未找到模型文件: {model_path}") return joblib.load(model_path) @app.context_processor def inject_helpers(): def now_year() -> int: return datetime.now().year return {"feature_list": FEATURES, "now_year": now_year} BASE_TEMPLATE_HEAD = r""" """ LOGIN_TEMPLATE = r""" {{ '注册' if mode == 'register' else '登录' }} | """ + BASE_TEMPLATE_HEAD + r"""

系统门户

登录 注册
{% with messages = get_flashed_messages(with_categories=true) %} {% if messages %}
{% for category, message in messages %}
{{ message }}
{% endfor %}
{% endif %} {% endwith %} {% if mode == 'login' %}
person
找回密码
lock
verified_user
{{ captcha }}
refresh
{% else %}
badge
lock
{% endif %}
""" DASHBOARD_TEMPLATE = r""" """ + BASE_TEMPLATE_HEAD + r"""
water_drop 供水管道健康评估系统
notifications help
{{ current_user.username[:1]|upper }}
退出登录

供水管道健康状态与剩余寿命评估技术导则

上传您的数据以生成预测结果供参考。

{% with messages = get_flashed_messages(with_categories=true) %} {% if messages %}
{% for category, message in messages %}
{{ message }}
{% endfor %}
{% endif %} {% endwith %}

upload_file 文件上传

download 下载模板
monitoring

进入结果页

查看完整的健康评估与重要性分析

chevron_right
""" RESULT_TEMPLATE = r""" 预测结果 """ + BASE_TEMPLATE_HEAD + r"""
water_drop 供水管道健康评估系统
notifications help
{{ current_user.username[:1]|upper }}
退出登录
{% if result %}
description
数据分析报告: {{ result.original_filename }}
基于上传数据生成的实时分析报告 • 生成于: {{ result.generated_at }}
check_circle数据源已验证
返回主页

预测结果

refresh重新运行分析 download导出预测报告 (Excel)

供水管道健康状态评估等级

I级
(0, 0.2]
管道安全风险十分严重，需立刻进行抢修或更新改造
II级
(0.2, 0.4]
管道安全风险较为严重，需尽快安排检修及加频巡检
III级
(0.4, 0.6]
管道安全风险较低，需安排定期巡检
IV级
(0.6, 0.8]
管道安全风险较小，维持常规巡视
V级
(0.8, 1]
管道安全，维持常规巡视
{% for item in result.summary_rows %} {% endfor %}
管道编号 健康等级
{{ item.pipe_id }} {{ item.grade_label }}
共 {{ result.sample_count }} 个样本，显示 {{ result.summary_rows|length }} 条 查看完整样本列表

管道剩余寿命动态评估

生存曲线拟合

生存概率阶梯图
info

分析说明：{{ result.analysis_text }}

{% if result.importance_url %}

模型输入因素重要性排序

各输入因素对预测结果的相对影响程度

模型输入因素重要性排序图
insights

说明：柱状图按重要性从高到低展示各输入因素对模型预测结果的相对贡献，数值为归一化占比，可用于辅助识别影响管道健康状态的关键因素。

{% endif %} {% else %}
当前还没有预测结果，请先从主页上传文件并运行预测。
{% endif %}
""" ADMIN_TEMPLATE = r""" 管理台 """ + BASE_TEMPLATE_HEAD + r"""

管理员查看上传记录

返回主页
{% for record in records %} {% else %} {% endfor %}
用户 原始文件 上传时间 下载
{{ record.user.username }} {{ record.original_filename }} {{ record.upload_time.strftime('%Y-%m-%d %H:%M:%S') }} 原始文件 预测结果
暂无上传记录
""" HISTORY_TEMPLATE = r""" 预测历史 """ + BASE_TEMPLATE_HEAD + r"""

预测历史

返回主页
{% for record in records %}
{{ record.original_filename }}
{{ record.upload_time.strftime('%Y-%m-%d %H:%M:%S') }}
原始文件 预测结果
{% else %}
还没有历史记录。
{% endfor %}
""" 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 ( f"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。" ) def compute_feature_importance(model, x_test: "pd.DataFrame") -> "np.ndarray | None": """返回各输入因素的相对重要性（与 FEATURES 顺序一致，归一化为占比）。 优先使用模型自带的 feature_importances_；若不可用，则采用与标签无关的 置换重要性：打乱单个特征后观察模型风险评分的平均变化幅度。 """ 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) n_repeats = 5 vals = np.zeros(len(FEATURES), dtype=float) for j, feat in enumerate(FEATURES): diffs = [] for _ in range(n_repeats): 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 # 仅保留有实际贡献的因素（占比 >= 0.05%，即不会显示为 0.0%）；若全部过低则回退展示全部 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) height = max(3.0, 0.62 * n + 1.6) fig, ax = plt.subplots(figsize=(9, height)) cmap = LinearSegmentedColormap.from_list("brand", ["#7fb2e6", "#005EB8", "#0c4188"]) colors = cmap(np.linspace(0.15, 1.0, n)) if n else None bars = ax.barh(sorted_feats, sorted_vals, color=colors, height=0.66, edgecolor="white", linewidth=0.8, zorder=3) ax.set_xlabel("相对重要性", fontsize=11, color="#475569") ax.set_title("模型输入因素重要性排序", fontsize=15, fontweight="bold", color="#0f172a", pad=14) 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) max_val = float(sorted_vals.max()) if n else 0.0 for bar, v in zip(bars, sorted_vals): ax.text(bar.get_width() + max_val * 0.012, bar.get_y() + bar.get_height() / 2, f"{v:.1%}", va="center", ha="left", fontsize=10, fontweight="bold", color="#1e293b") 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) @app.route("/") def index(): if current_user.is_authenticated: return redirect(url_for("home")) return redirect(url_for("login")) @app.route("/login", methods=["GET", "POST"]) def login(): if current_user.is_authenticated: return redirect(url_for("home")) if request.method == "GET": session["captcha"] = "".join(random.choices("ABCDEFGHJKLMNPQRSTUVWXYZ23456789", k=5)) return render_template_string(LOGIN_TEMPLATE, mode="login", captcha=session["captcha"]) username = request.form.get("username", "").strip() password = request.form.get("password", "") captcha_input = request.form.get("captcha", "").strip().upper() if captcha_input != session.get("captcha", ""): flash("验证码错误", "error") session["captcha"] = "".join(random.choices("ABCDEFGHJKLMNPQRSTUVWXYZ23456789", k=5)) return render_template_string(LOGIN_TEMPLATE, mode="login", captcha=session["captcha"]), 400 user = User.query.filter_by(username=username).first() if not user or not user.check_password(password): flash("用户名或密码错误", "error") session["captcha"] = "".join(random.choices("ABCDEFGHJKLMNPQRSTUVWXYZ23456789", k=5)) return render_template_string(LOGIN_TEMPLATE, mode="login", captcha=session["captcha"]), 400 login_user(user, remember=bool(request.form.get("remember"))) return redirect(url_for("home")) @app.route("/register", methods=["GET", "POST"]) def register(): if request.method == "GET": return render_template_string(LOGIN_TEMPLATE, mode="register", captcha="") username = request.form.get("username", "").strip() password = request.form.get("password", "") if not username: flash("用户名不能为空", "error") return render_template_string(LOGIN_TEMPLATE, mode="register", captcha=""), 400 if len(password) < 6: flash("密码至少需要 6 位", "error") return render_template_string(LOGIN_TEMPLATE, mode="register", captcha=""), 400 if User.query.filter_by(username=username).first(): flash("用户名已存在", "error") return render_template_string(LOGIN_TEMPLATE, mode="register", captcha=""), 400 user = User(username=username, is_admin=False) user.set_password(password) db.session.add(user) db.session.commit() flash("注册成功，请登录", "info") session["captcha"] = "".join(random.choices("ABCDEFGHJKLMNPQRSTUVWXYZ23456789", k=5)) return render_template_string(LOGIN_TEMPLATE, mode="login", captcha=session["captcha"]) @app.route("/logout") @login_required def logout(): logout_user() return redirect(url_for("login")) @app.route("/home") @login_required def home(): return render_template_string(DASHBOARD_TEMPLATE) @app.route("/history") @login_required def history_page(): records = UploadRecord.query.filter_by(user_id=current_user.id).order_by(UploadRecord.upload_time.desc()).all() return render_template_string(HISTORY_TEMPLATE, records=records) @app.route("/admin") @login_required def admin_dashboard(): if not current_user.is_admin: abort(403) records = UploadRecord.query.order_by(UploadRecord.upload_time.desc()).all() return render_template_string(ADMIN_TEMPLATE, records=records) @app.route("/download//") @login_required def download_file(record_id: int, file_type: str): record = UploadRecord.query.get_or_404(record_id) if not (current_user.is_admin or current_user.id == record.user_id): abort(403) file_path = record.saved_path if file_type == "original" else record.prediction_path if not os.path.exists(file_path): abort(404) return send_file(file_path, as_attachment=True) @app.route("/download_template") 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") @app.route("/result") @login_required def result_page(): result = session.get("last_result") return render_template_string(RESULT_TEMPLATE, result=result) @app.route("/predict", methods=["POST"]) @login_required def predict(): model = app.config.get("RSF_MODEL") if model is None: return jsonify({"error": "模型未成功加载，请检查 my_survival_forest_model_quxi-10-0331.joblib 文件。"}), 500 uploaded = request.files.get("file") if uploaded is None or uploaded.filename == "": return jsonify({"error": "未选择文件"}), 400 filename = secure_filename(uploaded.filename) if not filename.lower().endswith((".csv", ".xls", ".xlsx")): return jsonify({"error": "不支持的格式，仅支持 CSV / XLS / XLSX"}), 400 user_dir = UPLOAD_DIR / f"user_{current_user.id}" user_dir.mkdir(parents=True, exist_ok=True) timestamp = datetime.now().strftime("%Y%m%d%H%M%S") stem, ext = os.path.splitext(filename) original_path = user_dir / f"{stem}_{timestamp}{ext}" uploaded.save(original_path) try: if ext.lower() == ".csv": df = pd.read_csv(original_path) else: df = pd.read_excel(original_path) except Exception as exc: logging.exception("文件解析失败: %s", exc) return jsonify({"error": "文件解析失败，请检查编码或表格格式。"}), 400 missing = [col for col in FEATURES if col not in df.columns] if missing: return jsonify({"error": f"缺少必要字段: {', '.join(missing)}"}), 400 x_test = df[FEATURES].copy() try: curves = model.predict_survival_function(x_test) except Exception as exc: logging.exception("预测失败: %s", exc) return jsonify({"error": "模型预测失败，请检查输入字段类型是否正确。"}), 500 plt.figure(figsize=(10, 5.6)) summary_rows: List[Dict[str, Any]] = [] summary_sheet_rows: List[Dict[str, Any]] = [] 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]["Status"]) if "Status" in df.columns and pd.notna(df.iloc[i]["Status"]) else f"Pipe_{i+1:03d}" pipe_age = f"{df.iloc[i]['Pipeage']} 年" if "Pipeage" in df.columns and pd.notna(df.iloc[i]["Pipeage"]) else "-" health_probability = interpolate_probability(times, probs, 10) 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, "remaining_life": remaining_life, "grade_label": grade_label, "grade_desc": grade_desc, "grade_class": grade_class, }) summary_sheet_rows.append({ "管道标识": i + 1, "健康等级": grade_label, }) plt.step(times, probs, where="post", linewidth=2, label=pipe_id) plt.xlabel("预测时间轴（年）") plt.ylabel("生存概率") plt.title("预测分析图") plt.grid(alpha=0.18) if len(summary_rows) <= 12: plt.legend(loc="best", fontsize=8) plt.tight_layout() image_filename = f"plot_{current_user.id}_{timestamp}.png" image_path = IMAGE_DIR / image_filename plt.savefig(image_path, dpi=160, bbox_inches="tight") plt.close() importance_filename = None try: importance_values = compute_feature_importance(model, x_test) if importance_values is not None: importance_filename = f"importance_{current_user.id}_{timestamp}.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) importance_filename = None excel_filename = f"{stem}_pre_{timestamp}.xlsx" excel_path = user_dir / excel_filename with pd.ExcelWriter(excel_path, engine="xlsxwriter") as writer: pd.DataFrame(summary_sheet_rows).to_excel(writer, sheet_name="结果摘要", index=False) 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"] out_df = pd.DataFrame({"时间（年）": times, f"{pipe_id}生存概率": probs}) out_df.to_excel(writer, sheet_name=f"样本{i+1}", index=False) record = UploadRecord( user_id=current_user.id, original_filename=filename, saved_path=str(original_path), prediction_path=str(excel_path), image_path=str(image_path), ) db.session.add(record) db.session.commit() analysis_text = make_analysis_text(summary_rows) last_result = { "original_filename": filename, "generated_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "image_url": url_for("static", filename=f"images/{image_filename}"), "importance_url": url_for("static", filename=f"images/{importance_filename}") if importance_filename else None, "excel_url": url_for("download_file", record_id=record.id, file_type="prediction"), "result_url": url_for("result_page"), "sample_count": int(len(summary_rows)), "summary_rows": summary_rows[:3], "analysis_text": analysis_text, } session["last_result"] = last_result return jsonify({ "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"], "original_filename": filename, }) if __name__ == "__main__": init_app() app.run(host="0.0.0.0", port=5005, debug=False, use_reloader=False)