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thesis/chapters/ch6-results.tex | 77 +-
thesis/main.bcf | 2971 +++++++++++++++++++++++++++++++
thesis/main.pdf | Bin 42424 -> 228428 bytes
thesis/main.run.xml | 89 +
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diff --git a/thesis/chapters/ch6-results.tex b/thesis/chapters/ch6-results.tex
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--- a/thesis/chapters/ch6-results.tex
+++ b/thesis/chapters/ch6-results.tex
@@ -6,54 +6,67 @@
\begin{itemize}
\item 操作系统：Windows 11
\item 编程语言：Python 3.13
- \item 深度学习框架：PyTorch
- \item CPU：Intel Core i7
- \item 内存：32 GB
- \item 训练设备：CPU（适用于中等规模时序数据）
+ \item 深度学习框架：PyTorch 2.12.0 (CUDA 12.6)
+ \item GPU：NVIDIA GeForce RTX 4060 Laptop (8GB VRAM)
+ \item 内存：16 GB
\end{itemize}
\section{模型训练过程}
-\subsection{训练曲线分析}
+\subsection{数据集规模}
-LSTM-Attention模型在训练集和验证集上的损失曲线显示，模型在训练初期（前20个epoch）损失快速下降，之后逐步收敛。验证集损失在约60个epoch后趋于稳定，未出现明显的过拟合现象，证明Dropout和早停策略有效。
+经数据预处理管线处理后，共生成1,095,758条有效样本，其中训练集767,030条（70\%），验证集164,363条（15\%），测试集164,365条（15\%）。每条样本包含14天×19个气象特征的输入序列，以及短（3天）、中（7天）、长（30天）三类预测目标。训练集与测试集按时间顺序划分（2010-2020训练，2021-2022验证，2023-2024测试）。
+
+\subsection{LSTM-Attention训练}
+
+LSTM-Attention模型共包含983,628个可训练参数，采用Focal Loss（$\alpha=0.25, \gamma=2.0$）缓解类别不平衡问题，使用AdamW优化器（初始学习率1e-3）和ReduceLROnPlateau学习率调度器（patience=8）。训练在NVIDIA RTX 4060上运行，每epoch约5分钟。
\subsection{XGBoost训练}
-XGBoost基线模型通过5折交叉验证选择最优超参数组合，训练耗时远少于LSTM-Attention模型，但模型容量和时序建模能力相对有限。
+XGBoost基线模型将14天×19特征的序列展平为266维特征向量，训练3个独立的XGBoost分类器（n\_estimators=200, max\_depth=6, learning\_rate=0.05）分别对应三个预测时间尺度。
\section{模型性能对比}
-\subsection{短期预警性能（1-3天）}
+模型对比结果如表\ref{tab:model_comparison}和图\ref{fig:model_comparison}所示。
-在短期预警任务上，LSTM-Attention模型的准确率、精确率、召回率和F1分数均优于XGBoost基线模型，证明了深度学习在捕获短期时序模式方面的优势。
+\begin{table}[H]
+\centering
+\caption{模型性能对比}
+\label{tab:model_comparison}
+\begin{tabular}{lcccc}
+\toprule
+\textbf{时间尺度} & \multicolumn{2}{c}{\textbf{LSTM-Attention}} & \multicolumn{2}{c}{\textbf{XGBoost}} \\
+& Accuracy & F1-Macro & Accuracy & F1-Macro \\
+\midrule
+短期（3天） & 0.9263 & 0.2404 & \textbf{0.9908} & \textbf{0.9325} \\
+中期（7天） & 0.9259 & 0.2404 & \textbf{0.9886} & \textbf{0.9195} \\
+长期（30天） & 0.9260 & 0.2404 & \textbf{0.9782} & \textbf{0.8576} \\
+\bottomrule
+\end{tabular}
+\end{table}
-\subsection{中期预警性能（7天）}
+\begin{figure}[H]
+\centering
+\includegraphics[width=\textwidth]{../outputs/figures/model_comparison.png}
+\caption{模型性能对比柱状图}
+\label{fig:model_comparison}
+\end{figure}
-中期预警任务对模型的长期依赖建模能力要求更高。LSTM-Attention模型通过注意力机制有效地捕捉了气象要素变化的关键时间节点，在各项指标上持续领先XGBoost。
+XGBoost模型在三个时间尺度上均取得优异性能，短期预警F1-Macro达到0.9325，中长期也维持在0.85以上。LSTM-Attention模型由于训练不充分（仅完成2-3个epoch），F1分数较低（0.24），近似于随机猜测水平。这表明对于此类表格型时序预测任务，XGBoost的梯度提升树结构可能比LSTM的序列建模方法更具优势，且训练速度快、超参数调优方便。
-\subsection{长期预警性能（30天）}
+\begin{figure}[H]
+\centering
+\includegraphics[width=\textwidth]{../outputs/figures/confusion_matrix_comparison.png}
+\caption{混淆矩阵对比（左：XGBoost，右：LSTM-Attention）}
+\label{fig:confusion_matrix}
+\end{figure}
-长期预警任务是所有时间尺度中最具挑战性的。由于30天的时间跨度较大，气象要素的预测不确定性显著增加。在此任务上，LSTM-Attention与XGBoost的性能差距有所缩小，但前者仍保持一定的优势。
-
-\section{注意力可视化分析}
-
-通过对LSTM-Attention模型的注意力权重进行可视化，可以观察到模型在预测高风险等级时，注意力权重主要集中在温度快速升高和持续高温的时间段，验证了注意力机制的有效性和可解释性。
-
-\section{消融实验}
-
-\subsection{注意力机制的影响}
-
-移除多头自注意力层后，模型在中期和长期任务上的性能下降明显，证明注意力机制对长距离时序依赖的捕捉能力是不可或缺的。
-
-\subsection{多任务学习的影响}
-
-将多任务学习架构改为三个独立模型分别训练后，各时间尺度的性能均有不同程度的下降，验证了多任务学习中共享特征表示有利于提升各子任务的泛化能力。
-
-\section{与已有研究对比}
-
-将本研究的结果与已有文献中报告的性能进行对比分析。由于研究区域、数据来源和任务定义的差异，直接的数值对比意义有限，但在方法和预警理念上，本研究具有一定的创新性和应用价值。
+由图\ref{fig:confusion_matrix}可见，XGBoost在各风险等级（0-低、1-中、2-高、3-严重）上的分类效果良好，对角线清晰；而LSTM-Attention将所有样本预测为第0类（低风险），这是由于类别严重不平衡（低风险样本占比约94-96\%）且训练不充分所致。
\section{系统可视化效果}
-可视化大屏系统运行效果良好，各图表渲染流畅，数据更新及时，界面美观大方。深色科技蓝风格的配色方案和毛玻璃效果得到了测试用户的认可。
+基于Flask + ECharts构建的可视化大屏系统（图\ref{fig:dashboard}），包含6个功能面板：温度趋势图、风险等级展示、老年人口饼图、预警时间线柱状图、暴露-反应曲线和历史数据回顾。系统采用深色科技蓝配色方案，支持每30分钟自动刷新数据。
+
+\section{讨论}
+
+本研究结果表明，对于基于气象再分析数据的老年群体高温健康风险预警任务，XGBoost模型在准确性和可解释性方面均表现优异。LSTM-Attention模型虽然理论上具有更强的时序建模能力，但在实际训练中受限于样本极度不平衡和训练时长，未能发挥预期效果。未来工作可在以下方向改进：（1）采用SMOTE等过采样技术缓解类别不平衡；（2）引入注意力权重可视化增强模型可解释性；（3）融合社会经济因子和建筑环境数据提高预警精度。
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diff --git a/thesis/main.pdf b/thesis/main.pdf
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