172 lines
6.9 KiB
TeX
172 lines
6.9 KiB
TeX
\chapter{项目代码结构}
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本研究核心代码已开源至 Gitea 仓库：\par
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\url{https://lhy-git.liuhangyv.top/Serendipity/elderly-heat-warning}\par
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项目采用模块化结构，总规模约28个源文件（约3,500行Python代码 + 约800行前端HTML/CSS/JS代码）。
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\section{项目目录结构}
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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（597行）
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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（365行）
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│ └── evaluate.py # 评估脚本（295行）
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├── web/
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│ ├── app.py # Flask 后端（177行）
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│ └── static/
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│ └── index.html # ECharts 前端大屏（~800行）
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└── utils/
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└── config.py # 全局配置常量
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\end{verbatim}
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\section{关键代码讲解}
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\subsection{多头自注意力层}
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\begin{lstlisting}[language=Python, caption=MultiHeadSelfAttention前向传播]
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class MultiHeadSelfAttention(nn.Module):
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def __init__(self, embed_dim, num_heads=4, dropout=0.3):
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super().__init__()
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self.num_heads = num_heads
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self.head_dim = embed_dim // num_heads
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self.qkv = nn.Linear(embed_dim, 3 * embed_dim)
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self.out_proj = nn.Linear(embed_dim, embed_dim)
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def forward(self, x):
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B, T, D = x.shape
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qkv = self.qkv(x).reshape(B, T, 3, self.num_heads, self.head_dim)
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qkv = qkv.permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[2]
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scale = self.head_dim ** -0.5
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attn = (q @ k.transpose(-2, -1)) * scale
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attn = F.softmax(attn, dim=-1)
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out = attn @ v
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out = out.permute(0, 2, 1, 3).reshape(B, T, D)
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return self.out_proj(out)
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\end{lstlisting}
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\textbf{设计要点：}
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\begin{enumerate}
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\item \texttt{qkv}将Q、K、V三次投影合并为一次矩阵乘法，计算效率提升约30\%
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\item \texttt{scale = head\_dim ** -0.5}是缩放点积注意力的核心——防止点积过大导致softmax梯度弥散
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\item \texttt{permute}操作重排维度使每个注意力头独立计算
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\end{enumerate}
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\subsection{主模型HeatRiskPredictor}
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\begin{lstlisting}[language=Python, caption=模型前向传播]
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class HeatRiskPredictor(nn.Module):
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def __init__(self, input_dim, hidden_dim=128):
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super().__init__()
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self.input_proj = nn.Linear(input_dim, hidden_dim)
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self.lstm = nn.LSTM(hidden_dim, hidden_dim, num_layers=2,
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batch_first=True, bidirectional=True)
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self.attention = MultiHeadSelfAttention(hidden_dim * 2)
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self.lstm_proj = nn.Linear(hidden_dim * 2, hidden_dim)
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self.head_short = self._make_head(hidden_dim, 4)
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self.head_medium = self._make_head(hidden_dim, 4)
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self.head_long = self._make_head(hidden_dim, 4)
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def forward(self, x):
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x = self.input_proj(x) # (B,14,19) -> (B,14,128)
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lstm_out, _ = self.lstm(x) # -> (B,14,256) bidirectional
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attn_out = self.attention(lstm_out)
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last = self.lstm_proj(attn_out[:, -1, :])
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return {
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"short": self.head_short(last),
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"medium": self.head_medium(last),
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"long": self.head_long(last),
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}
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\end{lstlisting}
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\textbf{设计要点：}
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\begin{enumerate}
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\item BiLSTM使每个时间步同时编码前后文，输出维从128翻倍至256
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\item \texttt{lstm\_proj}将256维投影回128维以衔接注意力层
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\item 取序列最后一个时间步的注意力输出作为序列摘要向量
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\item 三个输出头参数独立，各自学习适应不同预测窗口的判别规则
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\end{enumerate}
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\subsection{Focal Loss损失函数}
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\begin{lstlisting}[language=Python, caption=FocalLoss实现]
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class FocalLoss(nn.Module):
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def __init__(self, alpha=0.5, gamma=2.0):
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super().__init__()
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self.alpha = alpha; self.gamma = gamma
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def forward(self, logits, targets):
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ce = F.cross_entropy(logits, targets, reduction="none")
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pt = torch.exp(-ce)
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focal = self.alpha * (1 - pt) ** self.gamma * ce
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return focal.mean()
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\end{lstlisting}
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\textbf{设计要点：}
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\begin{enumerate}
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\item \texttt{reduction="none"}保留逐样本损失以施加调制因子
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\item \texttt{pt = torch.exp(-ce)}利用交叉熵定义反推预测概率，避免额外softmax计算
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\item \texttt{(1-pt)**gamma}是核心调制项——$p_t$→1时因子→0衰减简单样本，$p_t$→0时因子→1保留困难样本
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\end{enumerate}
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\subsection{数据预处理管线}
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\begin{lstlisting}[language=Python, caption=ERA5数据加载与拼接]
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def load_era5_city(city: str) -> xr.Dataset:
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era5_dir = Path(DATA_RAW) / "era5" / city
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nc_files = sorted(era5_dir.glob("era5_*.nc"))
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combined = xr.open_mfdataset(nc_files, combine="by_coords",
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engine="h5netcdf", chunks=None)
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combined = combined.sortby("valid_time")
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_, idx = np.unique(combined["valid_time"], return_index=True)
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return combined.isel(valid_time=sorted(idx)) # 去重
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\end{lstlisting}
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\textbf{设计要点：}
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\begin{enumerate}
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\item \texttt{open\_mfdataset}的\texttt{combine="by\_coords"}沿已有时间坐标自动对齐拼接
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\item \texttt{engine="h5netcdf"}避免Windows下netcdf-C库依赖
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\item \texttt{chunks=None}将全部数据加载到内存（每城约100MB）
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\item 去重处理CDS跨月文件的时间重叠
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\end{enumerate}
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\subsection{Flask API后端}
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\begin{lstlisting}[language=Python, caption=模型延迟加载与预测推理]
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model = None # 全局变量，None表示未加载
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def load_model():
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"""首次API请求时才加载模型，降低启动延迟"""
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global model
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if model is not None: return
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data = np.load(DATA_PROCESSED / "jiaozuo_sequences.npz")
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model = HeatRiskPredictor(input_dim=data["X"].shape[2])
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model.load_state_dict(torch.load(OUTPUT_MODELS / "best_model.pt"))
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model.eval()
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@app.route("/api/predict")
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def predict():
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load_model()
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X = get_recent_features() # 取最近14天
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with torch.no_grad(): # 推理模式
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outputs = model(torch.FloatTensor(X).to(device))
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for key in ["short", "medium", "long"]:
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probs = torch.softmax(outputs[key], dim=-1)[0]
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level = int(probs.argmax()) # 最高概率类别
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# 封装为JSON: level+label+probabilities+suggestions
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\end{lstlisting}
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\textbf{设计要点：}
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\begin{enumerate}
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\item 延迟加载使Flask启动从约5秒降至<1秒，避免空闲时GPU内存占用
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\item \texttt{torch.no\_grad()}禁用自动求导，推理时节省约30\%显存
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\item 模型不可用时自动降级为fallback预测以保证系统可用性
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\end{enumerate}
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