style: 按河南理工论文规范重排 — 封面/页眉页脚/标题字号/图表标题位置
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随着全球气候变暖,极端高温事件频发且强度持续增加,对公共卫生构成日益严峻的挑战。老年群体(65岁及以上)因体温调节功能减退、慢性病患病率高以及社会隔离等因素,是高温热浪最脆弱的群体之一。本研究以河南省焦作市和郑州市为研究区域,利用ERA5-Land气象再分析数据(2010-2024年),构建了基于机器学习的多时间尺度高温健康风险预警模型,并开发了Web可视化大屏系统。
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随着全球气候变暖,极端高温事件频发且强度持续增加,对公共卫生构成日益严峻的挑战。老年群体(65岁及以上)因体温调节功能减退、慢性病患病率高以及社会隔离等因素,是高温热浪最脆弱的群体之一。本研究以河南省焦作市和郑州市为研究区域,利用ERA5-Land气象再分析数据(2010-2024年),构建了基于机器学习的多时间尺度高温健康风险预警模型,并开发了Web可视化大屏系统。
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Driven by global warming, extreme heat events are increasing in both frequency and intensity, posing severe public health challenges. The elderly population (aged 65 and above) is among the most vulnerable groups due to diminished thermoregulation, high prevalence of chronic diseases, and social isolation. This study focuses on Jiaozuo and Zhengzhou in Henan Province, utilizing ERA5-Land meteorological reanalysis data (2010--2024) to develop machine-learning-based multi-time-scale heat health risk early warning models, complemented by a web visualization dashboard.
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Driven by global warming, extreme heat events are increasing in both frequency and intensity, posing severe public health challenges. The elderly population (aged 65 and above) is among the most vulnerable groups due to diminished thermoregulation, high prevalence of chronic diseases, and social isolation. This study focuses on Jiaozuo and Zhengzhou in Henan Province, utilizing ERA5-Land meteorological reanalysis data (2010--2024) to develop machine-learning-based multi-time-scale heat health risk early warning models, complemented by a web visualization dashboard.
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% --- 页面设置 ---
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% ==================== 页面设置 ====================
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% --- 字体 ---
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% 正文: 宋体(小四≈12pt) | 标题: 黑体 | 封面信息: 仿宋
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% ==================== 页眉页脚 ====================
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\fancyhead[CO]{\zihao{-5}\songti 银发群体高温多时间尺度预警和服务优化可视化研究} % 奇数页
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% ==================== 图表 ====================
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% ==================== 参考文献 (GB/T 7714) ====================
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% --- 超链接 ---
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% ==================== 超链接 ====================
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\usepackage[hidelinks]{hyperref}
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% ==================== 数学 ====================
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% --- 代码 ---
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% ==================== 代码 ====================
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% ==================== 其他 ====================
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% ==================== 标题格式(河南理工规范) ====================
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}
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\title{银发群体高温多时间尺度预警和服务优化可视化研究}
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\title{银发群体高温多时间尺度预警和服务优化可视化研究}
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\author{刘航宇}
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\author{刘航宇}
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\begin{document}
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\begin{document}
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% --- 封面 ---
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% ==================== 封面 ====================
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\begin{center}
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\begin{titlepage}
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\vspace*{3cm}
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{\large\bfseries 本科毕业论文}\\[1cm]
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{\LARGE\bfseries 银发群体高温多时间尺度预警\\[0.3cm]和服务优化可视化研究}\\[2cm]
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{\large 学\hspace{2em}院:计算机科学与技术学院}\\[0.5cm]
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{\large 专\hspace{2em}业:计算机科学与技术}\\[0.5cm]
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{\large 姓\hspace{2em}名:刘航宇}\\[0.5cm]
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{\large 学\hspace{2em}号:}\\[0.5cm]
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{\large 指导教师:}\\[2cm]
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{\large \today}
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\begin{center}
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\vspace*{2cm}
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{\zihao{1}\heiti\bfseries 本科毕业设计(论文)}\\[1.5cm]
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% --- 摘要 ---
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{\zihao{2}\heiti\bfseries 银发群体高温多时间尺度预警\\[0.2cm]和服务优化可视化研究}\\[3cm]
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{\zihao{4}\fangsong
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\begin{tabular}{rl}
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\textbf{学\hspace{2em}院:} & 计算机科学与技术学院 \\[0.3cm]
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\textbf{专\hspace{2em}业:} & 计算机科学与技术 \\[0.3cm]
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\textbf{姓\hspace{2em}名:} & 刘航宇 \\[0.3cm]
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\textbf{学\hspace{2em}号:} & \\[0.3cm]
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\textbf{指导教师:} & \\[1.5cm]
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\end{tabular}
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}
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\vfill
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{\zihao{4}\fangsong \today}
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\end{center}
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\end{titlepage}
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% ==================== 摘要 ====================
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\input{chapters/abstract}
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\input{chapters/abstract}
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% --- 目录 ---
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% ==================== 目录 ====================
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\pagestyle{fancy}
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\tableofcontents
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\tableofcontents
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\newpage
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\newpage
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% --- 正文 ---
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% ==================== 正文 ====================
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\input{chapters/ch1-intro}
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\input{chapters/ch1-intro}
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\input{chapters/ch2-theory}
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\input{chapters/ch2-theory}
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\input{chapters/ch3-data}
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\input{chapters/ch3-data}
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\input{chapters/ch6-results}
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\input{chapters/ch6-results}
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\input{chapters/ch7-conclusion}
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\input{chapters/ch7-conclusion}
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% --- 参考文献 ---
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% ==================== 参考文献 ====================
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\printbibliography[title=参考文献]
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\printbibliography[title=参考文献]
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% --- 致谢 ---
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% ==================== 致谢 ====================
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\chapter*{致谢}
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\chapter*{致谢}
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\addcontentsline{toc}{chapter}{致谢}
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感谢家人和朋友在学业期间的理解、支持与鼓励。
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感谢家人和朋友在学业期间的理解、支持与鼓励。
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% --- 附录 ---
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% ==================== 附录 ====================
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\appendix
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\appendix
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number=\Alph{chapter},
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},
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}
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\chapter{核心代码清单}
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\chapter{核心代码清单}
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本文核心代码已开源,完整项目结构及运行说明见附录B。
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本研究核心代码已开源至 Gitea 仓库 \texttt{git@lhy-git.liuhangyv.top:Serendipity/elderly-heat-warning.git}。项目采用模块化结构:
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\begin{verbatim}
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src/
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├── data/
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│ ├── download_era5.py # ERA5 数据下载(CDS API)
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│ ├── extract_zips.py # NetCDF ZIP 解压
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│ ├── preprocess.py # 数据预处理管线
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│ └── collect_mortality.py # 死亡率数据整理
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├── models/
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│ ├── lstm_attention.py # LSTM-Attention 模型定义
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│ ├── xgboost_baseline.py # XGBoost 基线
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│ ├── train.py # 训练脚本
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│ └── evaluate.py # 评估脚本
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├── web/
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│ ├── app.py # Flask 后端
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│ └── static/
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│ └── index.html # ECharts 前端大屏
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└── utils/
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└── config.py # 全局配置
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\end{verbatim}
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\chapter{系统运行说明}
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\chapter{系统运行说明}
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\section{环境配置}
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\section{环境配置}
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本项目使用 Python 3.13,依赖管理使用 uv。主要依赖包括 PyTorch、XGBoost、Flask、ECharts 等。
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\begin{itemize}
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\item Python 3.13
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\item PyTorch 2.12.0+cu126(GPU版本,CUDA 12.6)
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\item XGBoost 2.0+,Scikit-learn 1.3+
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\item Flask 3.0+,ECharts 5.5
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\item 环境管理:uv(虚拟环境 .venv)
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\end{itemize}
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\section{运行步骤}
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\section{运行步骤}
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\begin{enumerate}
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\begin{enumerate}
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\item 安装依赖:\texttt{uv pip install -e .}
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\item \textbf{安装依赖}:\texttt{uv pip install -e .}
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\item 数据获取:\texttt{python -m src.data.download\_era5}
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\item \textbf{下载 ERA5 数据}:\texttt{python -m src.data.download\_era5}
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\item 数据预处理:\texttt{python -m src.data.preprocess}
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\item \textbf{解压 ZIP 格式数据}:\texttt{python -m src.data.extract\_zips}
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\item 模型训练:\texttt{python -m src.models.train}
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\item \textbf{运行预处理}:\texttt{python -m src.data.preprocess}
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\item 启动可视化:\texttt{python -m src.web.app}
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\item \textbf{训练模型}:\texttt{python -m src.models.train}(LSTM)
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\item 浏览器访问:\texttt{http://localhost:5005}
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\item \textbf{评估模型}:\texttt{python -m src.models.evaluate}(含XGBoost)
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\item \textbf{启动可视化大屏}:\texttt{python -m src.web.app}
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\item \textbf{浏览器访问}:\texttt{http://localhost:5005}
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\end{enumerate}
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\end{enumerate}
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\end{document}
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\end{document}
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