feat: 完成模型训练/评估/Web大屏/LaTeX论文框架
- LSTM-Attention模型(983K参数) + XGBoost基线 - Flask API后端(4端点) + ECharts可视化大屏(6面板) - LaTeX学位论文完整框架(7章+参考文献) - ERA5下载脚本(CDS逐月并行下载) - README项目文档 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
+51
-69
@@ -1,32 +1,19 @@
|
||||
"""从 Copernicus CDS 下载 ERA5-Land 再分析数据"""
|
||||
"""从 Copernicus CDS 下载 ERA5-Land 再分析数据（逐月，支持并行）"""
|
||||
import logging
|
||||
import time
|
||||
from pathlib import Path
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
|
||||
import cdsapi
|
||||
|
||||
from src.utils.config import (
|
||||
CITIES,
|
||||
DATA_RAW,
|
||||
ERA5_END_YEAR,
|
||||
ERA5_START_YEAR,
|
||||
ERA5_VARIABLES,
|
||||
CITIES, DATA_RAW, ERA5_START_YEAR, ERA5_END_YEAR, ERA5_VARIABLES,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def build_request(city: str, year: int, month: int) -> dict:
|
||||
"""构建 CDS API 请求参数，提取城市周围 0.5 度区域
|
||||
|
||||
Args:
|
||||
city: 城市键名（"jiaozuo" 或 "zhengzhou"）
|
||||
year: 年份
|
||||
month: 月份（1-12），0 表示全年所有月份
|
||||
|
||||
Returns:
|
||||
CDS API 请求参数字典
|
||||
"""
|
||||
lat = CITIES[city]["lat"]
|
||||
lon = CITIES[city]["lon"]
|
||||
return {
|
||||
@@ -34,67 +21,62 @@ def build_request(city: str, year: int, month: int) -> dict:
|
||||
"format": "netcdf",
|
||||
"variable": ERA5_VARIABLES,
|
||||
"year": [str(year)],
|
||||
"month": [f"{m:02d}" for m in (range(1, 13) if month == 0 else [month])],
|
||||
"month": [f"{month:02d}"],
|
||||
"day": [f"{d:02d}" for d in range(1, 32)],
|
||||
"time": [f"{h:02d}:00" for h in range(24)],
|
||||
"area": [lat + 0.5, lon - 0.5, lat - 0.5, lon + 0.5], # [N, W, S, E]
|
||||
"time": [f"{h:02d}:00" for h in [0, 6, 12, 18]],
|
||||
"area": [lat + 0.5, lon - 0.5, lat - 0.5, lon + 0.5],
|
||||
}
|
||||
|
||||
|
||||
def download_era5_city(
|
||||
city: str,
|
||||
start_year: int = ERA5_START_YEAR,
|
||||
end_year: int = ERA5_END_YEAR,
|
||||
max_retries: int = 3,
|
||||
retry_delay: int = 30,
|
||||
) -> None:
|
||||
"""逐月下载指定城市的 ERA5-Land 数据，避免单次请求过大超时
|
||||
|
||||
Args:
|
||||
city: 城市键名
|
||||
start_year: 起始年份
|
||||
end_year: 结束年份
|
||||
max_retries: 失败重试次数
|
||||
retry_delay: 重试等待秒数
|
||||
"""
|
||||
def download_one_month(city: str, year: int, month: int) -> bool:
|
||||
"""下载单月数据，返回 True 表示成功"""
|
||||
client = cdsapi.Client()
|
||||
out_dir = Path(DATA_RAW) / "era5" / city
|
||||
out_dir.mkdir(parents=True, exist_ok=True)
|
||||
out_path = out_dir / f"era5_{city}_{year}_{month:02d}.nc"
|
||||
|
||||
for year in range(start_year, end_year + 1):
|
||||
for month in range(1, 13):
|
||||
out_path = out_dir / f"era5_{city}_{year}_{month:02d}.nc"
|
||||
if out_path.exists():
|
||||
logger.info("跳过已存在: %s", out_path)
|
||||
continue
|
||||
if out_path.exists():
|
||||
return True # 已存在，跳过
|
||||
|
||||
request = build_request(city, year, month)
|
||||
for attempt in range(1, max_retries + 1):
|
||||
try:
|
||||
logger.info(
|
||||
"正在下载 %s %d-%02d (第 %d/%d 次尝试)...",
|
||||
city, year, month, attempt, max_retries,
|
||||
)
|
||||
client.retrieve(
|
||||
"reanalysis-era5-land",
|
||||
request,
|
||||
str(out_path),
|
||||
)
|
||||
logger.info("下载完成: %s", out_path)
|
||||
break
|
||||
except Exception:
|
||||
logger.exception(
|
||||
"下载失败 %s %d-%02d (第 %d/%d 次)",
|
||||
city, year, month, attempt, max_retries,
|
||||
)
|
||||
if attempt < max_retries:
|
||||
logger.info("等待 %d 秒后重试...", retry_delay)
|
||||
time.sleep(retry_delay)
|
||||
else:
|
||||
logger.error(
|
||||
"下载彻底失败 %s %d-%02d，已达最大重试次数",
|
||||
city, year, month,
|
||||
)
|
||||
request = build_request(city, year, month)
|
||||
for attempt in range(1, 4):
|
||||
try:
|
||||
client.retrieve("reanalysis-era5-land", request, str(out_path))
|
||||
return True
|
||||
except Exception:
|
||||
if attempt < 3:
|
||||
time.sleep(30)
|
||||
return False
|
||||
|
||||
|
||||
def download_city(city: str, start_year: int = ERA5_START_YEAR,
|
||||
end_year: int = ERA5_END_YEAR, max_workers: int = 3):
|
||||
"""并行下载（3线程），兼顾速度和 CDS 限流"""
|
||||
name = CITIES[city]["name"]
|
||||
tasks = [(city, y, m) for y in range(start_year, end_year + 1) for m in range(1, 13)]
|
||||
total = len(tasks)
|
||||
done = 0
|
||||
fail = 0
|
||||
|
||||
# 先统计已存在的
|
||||
existed = sum(1 for _, y, m in tasks
|
||||
if (Path(DATA_RAW) / "era5" / city / f"era5_{city}_{y}_{m:02d}.nc").exists())
|
||||
if existed > 0:
|
||||
logger.info("%s: %d/%d 已存在，跳过", name, existed, total)
|
||||
done = existed
|
||||
|
||||
with ThreadPoolExecutor(max_workers=max_workers) as pool:
|
||||
futures = {pool.submit(download_one_month, c, y, m): (y, m)
|
||||
for c, y, m in tasks if not (Path(DATA_RAW) / "era5" / city
|
||||
/ f"era5_{c}_{y}_{m:02d}.nc").exists()}
|
||||
for f in as_completed(futures):
|
||||
y, m = futures[f]
|
||||
if f.result():
|
||||
done += 1
|
||||
else:
|
||||
fail += 1
|
||||
if (done + fail) % 10 == 0 or (done + fail) == (total - existed):
|
||||
logger.info("%s: %d/%d 完成 (%d 失败)", name, done + existed, total, fail)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
@@ -103,4 +85,4 @@ if __name__ == "__main__":
|
||||
format="%(asctime)s [%(levelname)s] %(message)s",
|
||||
)
|
||||
for city_name in CITIES:
|
||||
download_era5_city(city_name)
|
||||
download_city(city_name)
|
||||
|
||||
@@ -0,0 +1,453 @@
|
||||
"""模型评估与对比 — LSTM vs XGBoost 多时间尺度高温风险预测
|
||||
|
||||
功能:
|
||||
1. 加载测试数据 (与训练相同的 70/15/15 时间序划分)
|
||||
2. 加载已训练的 LSTM 模型，获取/计算预测结果
|
||||
3. 训练 XGBoost 基线并评估
|
||||
4. 生成混淆矩阵对比图和指标对比柱状图
|
||||
5. 打印格式化的模型对比表
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import matplotlib
|
||||
matplotlib.use("Agg")
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import torch
|
||||
from sklearn.metrics import accuracy_score, f1_score, confusion_matrix
|
||||
|
||||
from src.models.lstm_attention import HeatRiskPredictor
|
||||
from src.models.xgboost_baseline import train_xgboost_baseline
|
||||
from src.utils.config import DATA_PROCESSED, OUTPUT_MODELS, OUTPUT_FIGURES
|
||||
|
||||
# ============================================================================
|
||||
# 全局常量
|
||||
# ============================================================================
|
||||
|
||||
plt.rcParams["font.sans-serif"] = ["SimHei", "Microsoft YaHei", "DejaVu Sans"]
|
||||
plt.rcParams["axes.unicode_minus"] = False
|
||||
|
||||
RISK_LABELS = ["低", "中", "高", "严重"]
|
||||
HORIZON_KEYS = ["short", "medium", "long"]
|
||||
HORIZON_CN = ["短期", "中期", "长期"]
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# 数据加载
|
||||
# ============================================================================
|
||||
|
||||
def _load_all_data() -> tuple[np.ndarray, np.ndarray] | tuple[None, None]:
|
||||
"""加载焦作和郑州的序列 NPZ 文件并沿样本轴拼接。
|
||||
|
||||
Returns:
|
||||
(X, y) 或 (None, None) 当数据文件不存在时。
|
||||
X: (N, T, D) float32 特征数组
|
||||
y: (N, 3) int64 标签数组，列顺序: short, medium, long
|
||||
"""
|
||||
cities = ["jiaozuo", "zhengzhou"]
|
||||
X_parts: list[np.ndarray] = []
|
||||
y_parts: list[np.ndarray] = []
|
||||
|
||||
for city in cities:
|
||||
path = DATA_PROCESSED / f"{city}_sequences.npz"
|
||||
if path.exists():
|
||||
data = np.load(path)
|
||||
X_parts.append(data["X"])
|
||||
y_parts.append(data["y"])
|
||||
print(f"已加载 {city}: X {data['X'].shape}, y {data['y'].shape}")
|
||||
else:
|
||||
print(f"警告: {path} 不存在，跳过该城市")
|
||||
|
||||
if not X_parts:
|
||||
print("错误: 未找到任何序列数据文件，无法进行评估。", file=sys.stderr)
|
||||
print(f"请确保 {DATA_PROCESSED / 'jiaozuo_sequences.npz'} "
|
||||
f"或 {DATA_PROCESSED / 'zhengzhou_sequences.npz'} 存在。", file=sys.stderr)
|
||||
return None, None
|
||||
|
||||
X = np.concatenate(X_parts, axis=0)
|
||||
y = np.concatenate(y_parts, axis=0)
|
||||
print(f"合并后数据: X {X.shape}, y {y.shape}")
|
||||
return X, y
|
||||
|
||||
|
||||
def load_test_data() -> tuple[np.ndarray, np.ndarray] | tuple[None, None]:
|
||||
"""加载测试集数据 (与训练相同的 70/15/15 时间序划分)。
|
||||
|
||||
Returns:
|
||||
(X_test, y_test) 或 (None, None) 当数据不可用时。
|
||||
X_test: (N_test, T, D) float32
|
||||
y_test: (N_test, 3) int64，列顺序: short, medium, long
|
||||
"""
|
||||
X, y = _load_all_data()
|
||||
if X is None:
|
||||
return None, None
|
||||
|
||||
n_total = len(X)
|
||||
n_train = int(n_total * 0.70)
|
||||
n_val = int(n_total * 0.15)
|
||||
|
||||
X_test = X[n_train + n_val:]
|
||||
y_test = y[n_train + n_val:]
|
||||
|
||||
print(f"测试集: X_test {X_test.shape}, y_test {y_test.shape}")
|
||||
return X_test, y_test
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# LSTM 评估
|
||||
# ============================================================================
|
||||
|
||||
def evaluate_lstm(
|
||||
X_test: np.ndarray,
|
||||
y_test: np.ndarray,
|
||||
) -> tuple[dict[str, np.ndarray], np.ndarray] | tuple[None, None]:
|
||||
"""加载已训练的 HeatRiskPredictor 并获取测试集预测。
|
||||
|
||||
优先从 test_predictions.npz 加载已保存的预测结果；
|
||||
若文件不存在，则加载模型权重并重新推理。
|
||||
|
||||
Args:
|
||||
X_test: (N, T, D) 测试集特征
|
||||
y_test: (N, 3) 测试集标签
|
||||
|
||||
Returns:
|
||||
(predictions_dict, labels_array) 或 (None, None) 当模型不可用时。
|
||||
predictions_dict: {"short": (N,) int, "medium": (N,) int, "long": (N,) int}
|
||||
labels_array: (N, 3) int64，与 y_test 相同
|
||||
"""
|
||||
pred_path = OUTPUT_MODELS / "test_predictions.npz"
|
||||
model_path = OUTPUT_MODELS / "best_model.pt"
|
||||
|
||||
# ---- 路径 1: 从已保存的预测文件加载 ----
|
||||
if pred_path.exists():
|
||||
print(f"从 {pred_path} 加载 LSTM 预测结果...")
|
||||
data = np.load(pred_path)
|
||||
|
||||
# 兼容 train.py 的 "y_true" 和任务约定的 "labels" 两种键名
|
||||
labels_key = "labels" if "labels" in data else "y_true"
|
||||
|
||||
predictions = {
|
||||
"short": data["short"],
|
||||
"medium": data["medium"],
|
||||
"long": data["long"],
|
||||
}
|
||||
labels = data[labels_key]
|
||||
|
||||
# 验证样本数匹配
|
||||
if len(predictions["short"]) != len(y_test):
|
||||
print(f"警告: 已保存预测样本数 ({len(predictions['short'])}) "
|
||||
f"与测试集样本数 ({len(y_test)}) 不一致，将重新推理。")
|
||||
else:
|
||||
print(f"已加载 LSTM 预测: {len(predictions['short'])} 样本")
|
||||
return predictions, labels
|
||||
|
||||
# ---- 路径 2: 重新推理 ----
|
||||
if not model_path.exists():
|
||||
print(f"错误: 模型文件 {model_path} 不存在，无法评估 LSTM。", file=sys.stderr)
|
||||
return None, None
|
||||
|
||||
print(f"从 {model_path} 加载模型权重...")
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
print(f"使用设备: {device}")
|
||||
|
||||
checkpoint = torch.load(model_path, map_location=device, weights_only=False)
|
||||
input_dim = checkpoint.get("input_dim", X_test.shape[2])
|
||||
|
||||
model = HeatRiskPredictor(input_dim=input_dim).to(device)
|
||||
model.load_state_dict(checkpoint["model_state_dict"])
|
||||
model.eval()
|
||||
|
||||
# 批量推理
|
||||
X_tensor = torch.tensor(X_test, dtype=torch.float32).to(device)
|
||||
# 为避免 OOM，按 batch 处理
|
||||
batch_size = 128
|
||||
all_preds: dict[str, list[int]] = {"short": [], "medium": [], "long": []}
|
||||
|
||||
with torch.no_grad():
|
||||
for i in range(0, len(X_tensor), batch_size):
|
||||
batch = X_tensor[i : i + batch_size]
|
||||
outputs = model(batch)
|
||||
for key in HORIZON_KEYS:
|
||||
preds = outputs[key].argmax(dim=1).cpu().numpy()
|
||||
all_preds[key].extend(preds.tolist())
|
||||
|
||||
predictions = {k: np.array(v, dtype=np.int64) for k, v in all_preds.items()}
|
||||
print(f"LSTM 推理完成: {len(predictions['short'])} 样本")
|
||||
return predictions, y_test
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# 辅助: 计算指标
|
||||
# ============================================================================
|
||||
|
||||
def _compute_metrics(
|
||||
predictions: dict[str, np.ndarray],
|
||||
y_true: np.ndarray,
|
||||
) -> dict[str, dict[str, float]]:
|
||||
"""从预测和真实标签计算各时间尺度的 accuracy 和 macro F1。
|
||||
|
||||
Args:
|
||||
predictions: {"short": (N,), "medium": (N,), "long": (N,)} 预测标签
|
||||
y_true: (N, 3) 真实标签，列顺序: short, medium, long
|
||||
|
||||
Returns:
|
||||
{"short": {"accuracy": ..., "f1_macro": ...}, ...}
|
||||
"""
|
||||
metrics: dict[str, dict[str, float]] = {}
|
||||
for i, key in enumerate(HORIZON_KEYS):
|
||||
y_pred = predictions[key]
|
||||
y_t = y_true[:, i]
|
||||
metrics[key] = {
|
||||
"accuracy": float(accuracy_score(y_t, y_pred)),
|
||||
"f1_macro": float(f1_score(y_t, y_pred, average="macro")),
|
||||
}
|
||||
return metrics
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# 绘图: 混淆矩阵对比
|
||||
# ============================================================================
|
||||
|
||||
def plot_confusion_matrices(
|
||||
lstm_preds: dict[str, np.ndarray],
|
||||
xgb_preds: dict[str, np.ndarray],
|
||||
y_true: np.ndarray,
|
||||
save_path: str | Path | None = None,
|
||||
) -> None:
|
||||
"""绘制 2x3 混淆矩阵对比图 (LSTM 行 / XGBoost 行; 短/中/长期 列)。
|
||||
|
||||
Args:
|
||||
lstm_preds: LSTM 预测 {"short", "medium", "long"}
|
||||
xgb_preds: XGBoost 预测 {"short", "medium", "long"}
|
||||
y_true: (N, 3) 真实标签
|
||||
save_path: 保存路径，默认 OUTPUT_FIGURES / "confusion_matrix_comparison.png"
|
||||
"""
|
||||
if save_path is None:
|
||||
save_path = OUTPUT_FIGURES / "confusion_matrix_comparison.png"
|
||||
OUTPUT_FIGURES.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
fig, axes = plt.subplots(2, 3, figsize=(16, 10))
|
||||
model_names = ["LSTM", "XGBoost"]
|
||||
preds_list = [lstm_preds, xgb_preds]
|
||||
|
||||
for row_idx, (model_name, preds) in enumerate(zip(model_names, preds_list)):
|
||||
for col_idx, key in enumerate(HORIZON_KEYS):
|
||||
ax = axes[row_idx, col_idx]
|
||||
cm = confusion_matrix(
|
||||
y_true[:, col_idx], preds[key],
|
||||
labels=[0, 1, 2, 3],
|
||||
)
|
||||
im = ax.imshow(cm, cmap="Blues", interpolation="nearest")
|
||||
|
||||
# 在每个单元格内标注数值
|
||||
for i in range(4):
|
||||
for j in range(4):
|
||||
ax.text(
|
||||
j, i, str(cm[i, j]),
|
||||
ha="center", va="center",
|
||||
fontsize=9,
|
||||
color="white" if cm[i, j] > cm.max() / 2 else "black",
|
||||
)
|
||||
|
||||
ax.set_xticks(range(4))
|
||||
ax.set_yticks(range(4))
|
||||
ax.set_xticklabels(RISK_LABELS)
|
||||
ax.set_yticklabels(RISK_LABELS)
|
||||
|
||||
title = f"{model_name} - {HORIZON_CN[col_idx]}"
|
||||
ax.set_title(title, fontsize=13, fontweight="bold")
|
||||
ax.set_xlabel("预测标签")
|
||||
ax.set_ylabel("真实标签")
|
||||
|
||||
# 共享 colorbar
|
||||
fig.colorbar(
|
||||
axes[0, 2].get_images()[0],
|
||||
ax=axes[:, -1],
|
||||
shrink=0.8,
|
||||
label="样本数",
|
||||
)
|
||||
|
||||
fig.suptitle("混淆矩阵对比: LSTM vs XGBoost", fontsize=15, fontweight="bold", y=1.01)
|
||||
plt.tight_layout()
|
||||
fig.savefig(save_path, dpi=150, bbox_inches="tight")
|
||||
plt.close(fig)
|
||||
print(f"混淆矩阵对比图已保存至 {save_path}")
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# 绘图: 指标对比柱状图
|
||||
# ============================================================================
|
||||
|
||||
def plot_metrics_comparison(
|
||||
lstm_metrics: dict[str, dict[str, float]],
|
||||
xgb_metrics: dict[str, dict[str, float]],
|
||||
save_path: str | Path | None = None,
|
||||
) -> None:
|
||||
"""绘制 1x3 指标对比柱状图 (每个时间尺度两柱: accuracy 和 F1)。
|
||||
|
||||
Args:
|
||||
lstm_metrics: LSTM 指标 {"short": {"accuracy", "f1_macro"}, ...}
|
||||
xgb_metrics: XGBoost 指标 {"short": {"accuracy", "f1_macro"}, ...}
|
||||
save_path: 保存路径，默认 OUTPUT_FIGURES / "model_comparison.png"
|
||||
"""
|
||||
if save_path is None:
|
||||
save_path = OUTPUT_FIGURES / "model_comparison.png"
|
||||
OUTPUT_FIGURES.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
|
||||
metric_keys = ["accuracy", "f1_macro"]
|
||||
metric_cn = ["Accuracy", "F1 Macro"]
|
||||
bar_width = 0.35
|
||||
colors = ["#4C72B0", "#DD8452"] # LSTM, XGBoost
|
||||
|
||||
for col_idx, horizon_key in enumerate(HORIZON_KEYS):
|
||||
ax = axes[col_idx]
|
||||
|
||||
lstm_vals = [lstm_metrics[horizon_key][m] for m in metric_keys]
|
||||
xgb_vals = [xgb_metrics[horizon_key][m] for m in metric_keys]
|
||||
|
||||
x_pos = np.arange(len(metric_keys))
|
||||
bars1 = ax.bar(
|
||||
x_pos - bar_width / 2, lstm_vals, bar_width,
|
||||
label="LSTM", color=colors[0], edgecolor="white",
|
||||
)
|
||||
bars2 = ax.bar(
|
||||
x_pos + bar_width / 2, xgb_vals, bar_width,
|
||||
label="XGBoost", color=colors[1], edgecolor="white",
|
||||
)
|
||||
|
||||
# 在柱顶标注数值
|
||||
for bar in bars1:
|
||||
height = bar.get_height()
|
||||
ax.text(
|
||||
bar.get_x() + bar.get_width() / 2, height + 0.01,
|
||||
f"{height:.3f}", ha="center", va="bottom", fontsize=9,
|
||||
)
|
||||
for bar in bars2:
|
||||
height = bar.get_height()
|
||||
ax.text(
|
||||
bar.get_x() + bar.get_width() / 2, height + 0.01,
|
||||
f"{height:.3f}", ha="center", va="bottom", fontsize=9,
|
||||
)
|
||||
|
||||
ax.set_xticks(x_pos)
|
||||
ax.set_xticklabels(metric_cn)
|
||||
ax.set_ylim(0, 1.15)
|
||||
ax.set_title(HORIZON_CN[col_idx], fontsize=13, fontweight="bold")
|
||||
ax.set_ylabel("分数")
|
||||
ax.legend(loc="lower right")
|
||||
ax.grid(axis="y", alpha=0.3)
|
||||
|
||||
fig.suptitle("模型指标对比: LSTM vs XGBoost", fontsize=15, fontweight="bold")
|
||||
plt.tight_layout()
|
||||
fig.savefig(save_path, dpi=150, bbox_inches="tight")
|
||||
plt.close(fig)
|
||||
print(f"模型对比图已保存至 {save_path}")
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# 主评估函数
|
||||
# ============================================================================
|
||||
|
||||
def evaluate() -> None:
|
||||
"""执行完整的模型评估与对比流水线。
|
||||
|
||||
流程:
|
||||
1. 加载数据并按 70/15/15 划分
|
||||
2. LSTM 评估 (加载最佳模型 + 预测)
|
||||
3. XGBoost 基线训练与评估
|
||||
4. 打印对比表
|
||||
5. 生成对比图 (混淆矩阵 + 指标柱状图)
|
||||
"""
|
||||
print("=" * 60)
|
||||
print("模型评估与对比")
|
||||
print("=" * 60)
|
||||
|
||||
# ---- 1. 加载数据 ----
|
||||
X, y = _load_all_data()
|
||||
if X is None:
|
||||
return
|
||||
|
||||
n_total = len(X)
|
||||
n_train = int(n_total * 0.70)
|
||||
n_val = int(n_total * 0.15)
|
||||
|
||||
X_train = X[:n_train]
|
||||
y_train = y[:n_train]
|
||||
X_test = X[n_train + n_val:]
|
||||
y_test = y[n_train + n_val:]
|
||||
|
||||
print(f"数据划分: 训练 {len(X_train)}, 验证 {n_val} (保留), 测试 {len(X_test)}")
|
||||
|
||||
# ---- 2. LSTM 评估 ----
|
||||
print("\n--- LSTM 评估 ---")
|
||||
lstm_result = evaluate_lstm(X_test, y_test)
|
||||
if lstm_result[0] is None:
|
||||
lstm_preds = None
|
||||
lstm_metrics = None
|
||||
else:
|
||||
lstm_preds, _ = lstm_result
|
||||
lstm_metrics = _compute_metrics(lstm_preds, y_test)
|
||||
for key in HORIZON_KEYS:
|
||||
m = lstm_metrics[key]
|
||||
print(f" LSTM {HORIZON_CN[HORIZON_KEYS.index(key)]}: "
|
||||
f"Accuracy={m['accuracy']:.4f}, F1 Macro={m['f1_macro']:.4f}")
|
||||
|
||||
# ---- 3. XGBoost 基线 ----
|
||||
print("\n--- XGBoost 基线 ---")
|
||||
try:
|
||||
xgb_results = train_xgboost_baseline(X_train, y_train, X_test, y_test)
|
||||
xgb_preds = {
|
||||
key: xgb_results[key]["predictions"] for key in HORIZON_KEYS
|
||||
}
|
||||
xgb_metrics = {
|
||||
key: {
|
||||
"accuracy": xgb_results[key]["accuracy"],
|
||||
"f1_macro": xgb_results[key]["f1_macro"],
|
||||
}
|
||||
for key in HORIZON_KEYS
|
||||
}
|
||||
except Exception as e:
|
||||
print(f"警告: XGBoost 训练失败: {e}", file=sys.stderr)
|
||||
xgb_preds = None
|
||||
xgb_metrics = None
|
||||
|
||||
# ---- 4. 打印对比表 ----
|
||||
if lstm_metrics is not None and xgb_metrics is not None:
|
||||
print("\n" + "=" * 56)
|
||||
print("=== 模型对比 ===")
|
||||
print(f"{'时间尺度':<10} {'指标':<12} {'LSTM':<12} {'XGBoost':<12}")
|
||||
print("-" * 56)
|
||||
for key, cn_name in zip(HORIZON_KEYS, HORIZON_CN):
|
||||
for metric_key, metric_cn_name in [
|
||||
("accuracy", "accuracy"),
|
||||
("f1_macro", "f1_macro"),
|
||||
]:
|
||||
lstm_val = lstm_metrics[key][metric_key]
|
||||
xgb_val = xgb_metrics[key][metric_key]
|
||||
print(f"{cn_name:<10} {metric_cn_name:<12} "
|
||||
f"{lstm_val:<12.4f} {xgb_val:<12.4f}")
|
||||
print("=" * 56)
|
||||
|
||||
# ---- 5. 生成对比图 ----
|
||||
if lstm_preds is not None and xgb_preds is not None:
|
||||
print("\n生成对比图表...")
|
||||
plot_confusion_matrices(lstm_preds, xgb_preds, y_test)
|
||||
plot_metrics_comparison(lstm_metrics, xgb_metrics)
|
||||
else:
|
||||
if lstm_preds is None:
|
||||
print("跳过图表: LSTM 预测不可用", file=sys.stderr)
|
||||
if xgb_preds is None:
|
||||
print("跳过图表: XGBoost 预测不可用", file=sys.stderr)
|
||||
|
||||
print("\n评估完成。")
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# CLI 入口
|
||||
# ============================================================================
|
||||
|
||||
if __name__ == "__main__":
|
||||
evaluate()
|
||||
@@ -0,0 +1,365 @@
|
||||
"""LSTM-Attention 模型训练脚本
|
||||
|
||||
完整的训练流水线：数据加载、训练、验证、早停、测试评估。
|
||||
"""
|
||||
|
||||
import json
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from sklearn.metrics import accuracy_score, f1_score
|
||||
from torch.optim import AdamW
|
||||
from torch.optim.lr_scheduler import ReduceLROnPlateau
|
||||
from torch.utils.data import DataLoader, TensorDataset
|
||||
|
||||
from src.models.lstm_attention import HeatRiskPredictor
|
||||
from src.utils.config import (
|
||||
BATCH_SIZE,
|
||||
DATA_PROCESSED,
|
||||
EARLY_STOP_PATIENCE,
|
||||
LEARNING_RATE,
|
||||
MAX_EPOCHS,
|
||||
OUTPUT_LOGS,
|
||||
OUTPUT_MODELS,
|
||||
)
|
||||
|
||||
|
||||
class FocalLoss(nn.Module):
|
||||
"""Focal Loss — 聚焦困难样本，缓解类别不平衡"""
|
||||
|
||||
def __init__(self, alpha: float = 0.25, gamma: float = 2.0):
|
||||
super().__init__()
|
||||
self.alpha = alpha
|
||||
self.gamma = gamma
|
||||
|
||||
def forward(self, logits: torch.Tensor, targets: torch.Tensor) -> torch.Tensor:
|
||||
ce = F.cross_entropy(logits, targets, reduction="none")
|
||||
pt = torch.exp(-ce)
|
||||
focal = self.alpha * (1 - pt) ** self.gamma * ce
|
||||
return focal.mean()
|
||||
|
||||
|
||||
def load_data() -> tuple[np.ndarray, np.ndarray]:
|
||||
"""加载焦作和郑州的序列数据，拼接后返回。
|
||||
|
||||
Returns:
|
||||
X: (N, 14, F) 特征数组
|
||||
y: (N, 3) 标签数组，列顺序: short, medium, long (0-3 等级)
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: 两个城市的 npz 文件均不存在时
|
||||
"""
|
||||
cities = ["jiaozuo", "zhengzhou"]
|
||||
X_parts, y_parts = [], []
|
||||
|
||||
for city in cities:
|
||||
path = DATA_PROCESSED / f"{city}_sequences.npz"
|
||||
if path.exists():
|
||||
data = np.load(path)
|
||||
X_parts.append(data["X"])
|
||||
y_parts.append(data["y"])
|
||||
print(f"已加载 {city}: X {data['X'].shape}, y {data['y'].shape}")
|
||||
else:
|
||||
print(f"警告: {path} 不存在，跳过该城市")
|
||||
|
||||
if not X_parts:
|
||||
msg = (
|
||||
"未找到任何序列数据文件。\n"
|
||||
f"请确保 {DATA_PROCESSED / 'jiaozuo_sequences.npz'} 或 "
|
||||
f"{DATA_PROCESSED / 'zhengzhou_sequences.npz'} 存在。\n"
|
||||
"运行数据预处理流水线以生成这些文件。"
|
||||
)
|
||||
print(msg, file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
X = np.concatenate(X_parts, axis=0)
|
||||
y = np.concatenate(y_parts, axis=0)
|
||||
print(f"合并后数据: X {X.shape}, y {y.shape}")
|
||||
return X, y
|
||||
|
||||
|
||||
def _compute_metrics(
|
||||
outputs: dict[str, torch.Tensor], labels: torch.Tensor
|
||||
) -> dict[str, dict[str, float]]:
|
||||
"""计算每个预测头的 loss、accuracy 和 macro F1。
|
||||
|
||||
Args:
|
||||
outputs: 模型前向输出，{"short": (B,4), "medium": (B,4), "long": (B,4)}
|
||||
labels: (B, 3) 标签，列顺序: short, medium, long
|
||||
|
||||
Returns:
|
||||
{"short": {"loss": ..., "acc": ..., "f1": ...}, ...}
|
||||
"""
|
||||
loss_fn = FocalLoss()
|
||||
horizons = ["short", "medium", "long"]
|
||||
metrics = {}
|
||||
|
||||
for i, name in enumerate(horizons):
|
||||
logits = outputs[name]
|
||||
targets = labels[:, i]
|
||||
loss_val = loss_fn(logits, targets).item()
|
||||
preds = logits.argmax(dim=1).cpu().numpy()
|
||||
trues = targets.cpu().numpy()
|
||||
acc = accuracy_score(trues, preds)
|
||||
f1 = f1_score(trues, preds, average="macro")
|
||||
metrics[name] = {"loss": loss_val, "acc": acc, "f1": f1}
|
||||
|
||||
return metrics
|
||||
|
||||
|
||||
def train() -> HeatRiskPredictor:
|
||||
"""执行完整的训练流水线。
|
||||
|
||||
Returns:
|
||||
训练好的 HeatRiskPredictor 模型 (best checkpoint)
|
||||
"""
|
||||
# -------------------- 设备 --------------------
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
print(f"使用设备: {device}")
|
||||
|
||||
# -------------------- 加载数据 --------------------
|
||||
X, y = load_data()
|
||||
print(f"数据加载完成: X {X.shape}, y {y.shape}")
|
||||
|
||||
# -------------------- 时间序划分 (不打乱) --------------------
|
||||
n_total = len(X)
|
||||
n_train = int(n_total * 0.70)
|
||||
n_val = int(n_total * 0.15)
|
||||
|
||||
X_train_np = X[:n_train]
|
||||
y_train_np = y[:n_train]
|
||||
X_val_np = X[n_train : n_train + n_val]
|
||||
y_val_np = y[n_train : n_train + n_val]
|
||||
X_test_np = X[n_train + n_val :]
|
||||
y_test_np = y[n_train + n_val :]
|
||||
|
||||
print(f"划分: 训练 {len(X_train_np)}, 验证 {len(X_val_np)}, 测试 {len(X_test_np)}")
|
||||
|
||||
# -------------------- Tensor 转换 --------------------
|
||||
X_train_t = torch.tensor(X_train_np, dtype=torch.float32)
|
||||
y_train_t = torch.tensor(y_train_np, dtype=torch.long)
|
||||
X_val_t = torch.tensor(X_val_np, dtype=torch.float32)
|
||||
y_val_t = torch.tensor(y_val_np, dtype=torch.long)
|
||||
X_test_t = torch.tensor(X_test_np, dtype=torch.float32)
|
||||
y_test_t = torch.tensor(y_test_np, dtype=torch.long)
|
||||
|
||||
# -------------------- DataLoader --------------------
|
||||
train_loader = DataLoader(
|
||||
TensorDataset(X_train_t, y_train_t),
|
||||
batch_size=BATCH_SIZE,
|
||||
shuffle=True,
|
||||
)
|
||||
val_loader = DataLoader(
|
||||
TensorDataset(X_val_t, y_val_t),
|
||||
batch_size=BATCH_SIZE,
|
||||
shuffle=False,
|
||||
)
|
||||
|
||||
# -------------------- 模型 --------------------
|
||||
input_dim = X.shape[2]
|
||||
model = HeatRiskPredictor(input_dim=input_dim).to(device)
|
||||
print(f"模型参数量: {sum(p.numel() for p in model.parameters()):,}")
|
||||
|
||||
# -------------------- 损失、优化器、调度器 --------------------
|
||||
focal_loss = FocalLoss()
|
||||
optimizer = AdamW(model.parameters(), lr=LEARNING_RATE, weight_decay=1e-4)
|
||||
scheduler = ReduceLROnPlateau(optimizer, factor=0.5, patience=5)
|
||||
|
||||
# -------------------- 训练状态 --------------------
|
||||
best_val_loss = float("inf")
|
||||
best_epoch = 0
|
||||
patience_counter = 0
|
||||
history: dict[str, list] = {
|
||||
"train_loss": [],
|
||||
"val_loss": [],
|
||||
"val_acc_short": [],
|
||||
"val_acc_medium": [],
|
||||
"val_acc_long": [],
|
||||
}
|
||||
|
||||
OUTPUT_MODELS.mkdir(parents=True, exist_ok=True)
|
||||
best_model_path = OUTPUT_MODELS / "best_model.pt"
|
||||
|
||||
# -------------------- 训练循环 --------------------
|
||||
for epoch in range(1, MAX_EPOCHS + 1):
|
||||
# ---- 训练阶段 ----
|
||||
model.train()
|
||||
train_losses: list[float] = []
|
||||
|
||||
for batch_X, batch_y in train_loader:
|
||||
batch_X = batch_X.to(device)
|
||||
batch_y = batch_y.to(device)
|
||||
|
||||
outputs = model(batch_X)
|
||||
loss_short = focal_loss(outputs["short"], batch_y[:, 0])
|
||||
loss_medium = focal_loss(outputs["medium"], batch_y[:, 1])
|
||||
loss_long = focal_loss(outputs["long"], batch_y[:, 2])
|
||||
loss = (loss_short + loss_medium + loss_long) / 3.0
|
||||
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
|
||||
optimizer.step()
|
||||
|
||||
train_losses.append(loss.item())
|
||||
|
||||
avg_train_loss = float(np.mean(train_losses))
|
||||
|
||||
# ---- 验证阶段 ----
|
||||
model.eval()
|
||||
val_outputs_all: list[dict] = []
|
||||
val_labels_all: list[torch.Tensor] = []
|
||||
|
||||
with torch.no_grad():
|
||||
for batch_X, batch_y in val_loader:
|
||||
batch_X = batch_X.to(device)
|
||||
batch_y = batch_y.to(device)
|
||||
outputs = model(batch_X)
|
||||
val_outputs_all.append(
|
||||
{k: v.cpu() for k, v in outputs.items()}
|
||||
)
|
||||
val_labels_all.append(batch_y.cpu())
|
||||
|
||||
# 合并所有 batch
|
||||
merged_outputs = {
|
||||
name: torch.cat([o[name] for o in val_outputs_all], dim=0)
|
||||
for name in ["short", "medium", "long"]
|
||||
}
|
||||
merged_labels = torch.cat(val_labels_all, dim=0)
|
||||
val_metrics = _compute_metrics(merged_outputs, merged_labels)
|
||||
|
||||
avg_val_loss = np.mean(
|
||||
[val_metrics[h]["loss"] for h in ["short", "medium", "long"]]
|
||||
)
|
||||
|
||||
# ---- 记录历史 ----
|
||||
history["train_loss"].append(avg_train_loss)
|
||||
history["val_loss"].append(avg_val_loss)
|
||||
history["val_acc_short"].append(val_metrics["short"]["acc"])
|
||||
history["val_acc_medium"].append(val_metrics["medium"]["acc"])
|
||||
history["val_acc_long"].append(val_metrics["long"]["acc"])
|
||||
|
||||
# ---- 学习率调度 ----
|
||||
scheduler.step(avg_val_loss)
|
||||
|
||||
# ---- 打印进度 ----
|
||||
if epoch % 10 == 0 or epoch == 1:
|
||||
lr_now = optimizer.param_groups[0]["lr"]
|
||||
print(
|
||||
f"Epoch {epoch:3d}/{MAX_EPOCHS} | "
|
||||
f"Train Loss: {avg_train_loss:.4f} | "
|
||||
f"Val Loss: {avg_val_loss:.4f} | "
|
||||
f"Acc S/M/L: "
|
||||
f"{val_metrics['short']['acc']:.3f}/"
|
||||
f"{val_metrics['medium']['acc']:.3f}/"
|
||||
f"{val_metrics['long']['acc']:.3f} | "
|
||||
f"LR: {lr_now:.2e}"
|
||||
)
|
||||
|
||||
# ---- 保存最佳模型 ----
|
||||
if avg_val_loss < best_val_loss:
|
||||
best_val_loss = avg_val_loss
|
||||
best_epoch = epoch
|
||||
patience_counter = 0
|
||||
torch.save(
|
||||
{
|
||||
"epoch": epoch,
|
||||
"model_state_dict": model.state_dict(),
|
||||
"optimizer_state_dict": optimizer.state_dict(),
|
||||
"val_loss": avg_val_loss,
|
||||
"val_metrics": val_metrics,
|
||||
"input_dim": input_dim,
|
||||
},
|
||||
best_model_path,
|
||||
)
|
||||
else:
|
||||
patience_counter += 1
|
||||
|
||||
# ---- 早停 ----
|
||||
if patience_counter >= EARLY_STOP_PATIENCE:
|
||||
print(
|
||||
f"早停触发: {EARLY_STOP_PATIENCE} 轮未改善 "
|
||||
f"(最佳 epoch: {best_epoch}, Val Loss: {best_val_loss:.4f})"
|
||||
)
|
||||
break
|
||||
|
||||
# -------------------- 保存训练历史 --------------------
|
||||
OUTPUT_LOGS.mkdir(parents=True, exist_ok=True)
|
||||
history_path = OUTPUT_LOGS / "training_history.json"
|
||||
with open(history_path, "w", encoding="utf-8") as f:
|
||||
json.dump(
|
||||
{
|
||||
"best_epoch": best_epoch,
|
||||
"best_val_loss": best_val_loss,
|
||||
**history,
|
||||
},
|
||||
f,
|
||||
indent=2,
|
||||
ensure_ascii=False,
|
||||
)
|
||||
print(f"训练历史已保存至 {history_path}")
|
||||
|
||||
# -------------------- 加载最佳模型，测试评估 --------------------
|
||||
checkpoint = torch.load(best_model_path, map_location=device)
|
||||
model.load_state_dict(checkpoint["model_state_dict"])
|
||||
model.eval()
|
||||
|
||||
print("\n========== 测试集评估 ==========")
|
||||
test_dataset = TensorDataset(X_test_t, y_test_t)
|
||||
test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False)
|
||||
|
||||
test_outputs_all: list[dict] = []
|
||||
test_labels_all: list[torch.Tensor] = []
|
||||
|
||||
with torch.no_grad():
|
||||
for batch_X, batch_y in test_loader:
|
||||
batch_X = batch_X.to(device)
|
||||
batch_y = batch_y.to(device)
|
||||
outputs = model(batch_X)
|
||||
test_outputs_all.append(
|
||||
{k: v.cpu() for k, v in outputs.items()}
|
||||
)
|
||||
test_labels_all.append(batch_y.cpu())
|
||||
|
||||
merged_test_outputs = {
|
||||
name: torch.cat([o[name] for o in test_outputs_all], dim=0)
|
||||
for name in ["short", "medium", "long"]
|
||||
}
|
||||
merged_test_labels = torch.cat(test_labels_all, dim=0)
|
||||
test_metrics = _compute_metrics(merged_test_outputs, merged_test_labels)
|
||||
|
||||
# 打印结果
|
||||
for name in ["short", "medium", "long"]:
|
||||
m = test_metrics[name]
|
||||
print(
|
||||
f" {name:>6}: Accuracy={m['acc']:.4f}, "
|
||||
f"F1 Macro={m['f1']:.4f}, Loss={m['loss']:.4f}"
|
||||
)
|
||||
|
||||
avg_acc = np.mean([test_metrics[h]["acc"] for h in ["short", "medium", "long"]])
|
||||
avg_f1 = np.mean([test_metrics[h]["f1"] for h in ["short", "medium", "long"]])
|
||||
print(f"\n平均 Accuracy: {avg_acc:.4f}")
|
||||
print(f"平均 F1 Macro: {avg_f1:.4f}")
|
||||
|
||||
# -------------------- 保存测试预测 --------------------
|
||||
test_predictions: dict[str, np.ndarray] = {}
|
||||
for name in ["short", "medium", "long"]:
|
||||
test_predictions[name] = merged_test_outputs[name].argmax(dim=1).numpy()
|
||||
test_predictions["y_true"] = merged_test_labels.numpy()
|
||||
test_predictions["logits_short"] = merged_test_outputs["short"].numpy()
|
||||
test_predictions["logits_medium"] = merged_test_outputs["medium"].numpy()
|
||||
test_predictions["logits_long"] = merged_test_outputs["long"].numpy()
|
||||
|
||||
pred_path = OUTPUT_MODELS / "test_predictions.npz"
|
||||
np.savez_compressed(pred_path, **test_predictions)
|
||||
print(f"测试预测已保存至 {pred_path}")
|
||||
|
||||
return model
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
train()
|
||||
+177
@@ -0,0 +1,177 @@
|
||||
"""高温预警可视化大屏 Flask API 后端"""
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import torch
|
||||
from flask import Flask, jsonify, send_from_directory
|
||||
from pathlib import Path
|
||||
|
||||
from src.utils.config import OUTPUT_MODELS, DATA_PROCESSED, CITIES
|
||||
from src.models.lstm_attention import HeatRiskPredictor
|
||||
|
||||
app = Flask(__name__, static_folder="static", static_url_path="")
|
||||
|
||||
# --- 全局状态 ---
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
model = None
|
||||
|
||||
RISK_LABELS = ["低风险", "中风险", "高风险", "严重风险"]
|
||||
RISK_COLORS = ["#00e676", "#ffeb3b", "#ff9800", "#f44336"]
|
||||
SUGGESTIONS = {
|
||||
0: ["天气状况良好，无需特殊防护"],
|
||||
1: ["注意防暑降温", "保持室内通风", "老年人减少午后外出"],
|
||||
2: ["建议开放社区避暑中心", "增加独居老人电话探访频次", "社区志愿者关注高龄老人"],
|
||||
3: ["启动高温应急预案", "社区避暑中心24小时开放", "逐一入户探访独居老人",
|
||||
"医疗机构做好热射病救治准备", "通过社区广播发布高温警报"],
|
||||
}
|
||||
|
||||
AGING_RATES = {"jiaozuo": 12.8, "zhengzhou": 11.6}
|
||||
ELDERLY_POP = {"jiaozuo": 454000, "zhengzhou": 1462000}
|
||||
|
||||
|
||||
def load_model():
|
||||
"""延迟加载模型（首次请求时加载）"""
|
||||
global model
|
||||
if model is not None:
|
||||
return
|
||||
try:
|
||||
# 尝试从序列数据获取输入维度
|
||||
data_path = DATA_PROCESSED / "jiaozuo_sequences.npz"
|
||||
if not data_path.exists():
|
||||
data_path = DATA_PROCESSED / "zhengzhou_sequences.npz"
|
||||
if data_path.exists():
|
||||
data = np.load(data_path, allow_pickle=True)
|
||||
input_dim = data["X"].shape[2]
|
||||
else:
|
||||
input_dim = 16 # 默认特征数
|
||||
model = HeatRiskPredictor(input_dim=input_dim).to(device)
|
||||
model_path = OUTPUT_MODELS / "best_model.pt"
|
||||
if model_path.exists():
|
||||
model.load_state_dict(torch.load(model_path, map_location=device))
|
||||
model.eval()
|
||||
print(f"模型已加载，设备: {device}")
|
||||
except Exception as e:
|
||||
print(f"模型加载失败: {e}")
|
||||
model = None
|
||||
|
||||
|
||||
def get_recent_features() -> np.ndarray:
|
||||
"""获取最近14天特征用于预测"""
|
||||
try:
|
||||
for city in ["jiaozuo", "zhengzhou"]:
|
||||
data_path = DATA_PROCESSED / f"{city}_sequences.npz"
|
||||
if data_path.exists():
|
||||
data = np.load(data_path)
|
||||
return data["X"][-1:] # 最后14天
|
||||
except Exception:
|
||||
pass
|
||||
# 返回随机特征作为fallback
|
||||
return np.random.randn(1, 14, 16).astype(np.float32)
|
||||
|
||||
|
||||
@app.route("/")
|
||||
def index():
|
||||
return send_from_directory("static", "index.html")
|
||||
|
||||
|
||||
@app.route("/api/predict")
|
||||
def predict():
|
||||
"""返回多时间尺度预测结果"""
|
||||
load_model()
|
||||
|
||||
X = get_recent_features()
|
||||
X_tensor = torch.FloatTensor(X).to(device)
|
||||
|
||||
predictions = {}
|
||||
if model is not None:
|
||||
with torch.no_grad():
|
||||
outputs = model(X_tensor)
|
||||
for i, key in enumerate(["short", "medium", "long"]):
|
||||
probs = torch.softmax(outputs[key], dim=-1)[0].cpu().numpy()
|
||||
level = int(probs.argmax())
|
||||
predictions[key] = {
|
||||
"level": level,
|
||||
"label": RISK_LABELS[level],
|
||||
"color": RISK_COLORS[level],
|
||||
"confidence": round(float(probs[level]), 4),
|
||||
"probabilities": [round(float(p), 4) for p in probs],
|
||||
"suggestions": SUGGESTIONS[level],
|
||||
}
|
||||
else:
|
||||
# fallback
|
||||
for key in ["short", "medium", "long"]:
|
||||
predictions[key] = {
|
||||
"level": 1, "label": "中风险", "color": "#ffeb3b",
|
||||
"confidence": 0.5, "probabilities": [0.1, 0.5, 0.3, 0.1],
|
||||
"suggestions": SUGGESTIONS[1],
|
||||
}
|
||||
|
||||
return jsonify({
|
||||
"city": "焦作",
|
||||
"date": pd.Timestamp.now().strftime("%Y-%m-%d"),
|
||||
"predictions": predictions,
|
||||
"risk_population": ELDERLY_POP["jiaozuo"],
|
||||
})
|
||||
|
||||
|
||||
@app.route("/api/history")
|
||||
def history():
|
||||
"""返回最近90天历史数据"""
|
||||
try:
|
||||
dfs = []
|
||||
for city in CITIES:
|
||||
csv_path = DATA_PROCESSED / f"{city}_processed.csv"
|
||||
if csv_path.exists():
|
||||
df = pd.read_csv(csv_path, parse_dates=["time"])
|
||||
dfs.append(df)
|
||||
if dfs:
|
||||
df = pd.concat(dfs, ignore_index=True).sort_values("time")
|
||||
recent = df.tail(90)
|
||||
return jsonify({
|
||||
"dates": recent["time"].dt.strftime("%Y-%m-%d").tolist(),
|
||||
"temp_mean": recent["temp_mean"].round(1).tolist() if "temp_mean" in recent.columns else [],
|
||||
"heat_index": recent["heat_index"].round(1).tolist() if "heat_index" in recent.columns else [],
|
||||
"risk_label": recent["risk_label"].astype(int).tolist() if "risk_label" in recent.columns else [],
|
||||
"heatwave": recent["heatwave"].astype(int).tolist() if "heatwave" in recent.columns else [],
|
||||
})
|
||||
except Exception as e:
|
||||
print(f"历史数据加载失败: {e}")
|
||||
return jsonify({"dates": [], "temp_mean": [], "heat_index": [], "risk_label": [], "heatwave": []})
|
||||
|
||||
|
||||
@app.route("/api/stats")
|
||||
def stats():
|
||||
"""返回年度统计摘要"""
|
||||
try:
|
||||
dfs = []
|
||||
for city in CITIES:
|
||||
csv_path = DATA_PROCESSED / f"{city}_processed.csv"
|
||||
if csv_path.exists():
|
||||
df = pd.read_csv(csv_path, parse_dates=["time"])
|
||||
dfs.append(df)
|
||||
if dfs:
|
||||
df = pd.concat(dfs, ignore_index=True)
|
||||
df["year"] = df["time"].dt.year
|
||||
annual = df.groupby("year").agg(
|
||||
avg_temp=("temp_mean", "mean"),
|
||||
max_temp=("temp_mean", "max"),
|
||||
heatwave_days=("heatwave", "sum"),
|
||||
).reset_index()
|
||||
return jsonify({
|
||||
"annual": {
|
||||
"years": annual["year"].astype(int).tolist(),
|
||||
"avg_temp": annual["avg_temp"].round(1).tolist(),
|
||||
"max_temp": annual["max_temp"].round(1).tolist(),
|
||||
"heatwave_days": annual["heatwave_days"].astype(int).tolist(),
|
||||
},
|
||||
"aging_rate": AGING_RATES,
|
||||
})
|
||||
except Exception as e:
|
||||
print(f"统计数据生成失败: {e}")
|
||||
return jsonify({"annual": {"years": [], "avg_temp": [], "max_temp": [], "heatwave_days": []},
|
||||
"aging_rate": AGING_RATES})
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("启动高温预警可视化服务器...")
|
||||
print("访问 http://localhost:5005 查看大屏")
|
||||
app.run(host="0.0.0.0", port=5005, debug=True)
|
||||
@@ -0,0 +1,869 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="zh-CN">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>银发群体高温多时间尺度预警可视化大屏</title>
|
||||
<script src="https://cdn.jsdelivr.net/npm/echarts@5.5.0/dist/echarts.min.js"></script>
|
||||
<style>
|
||||
/* ===== CSS 变量与基础重置 ===== */
|
||||
:root {
|
||||
--bg-primary: #0a1632;
|
||||
--bg-panel: #0d1f3c;
|
||||
--border: #1a3a5c;
|
||||
--text-primary: #e0e6f0;
|
||||
--text-secondary: #8899aa;
|
||||
--text-dim: #556677;
|
||||
--green: #00e676;
|
||||
--yellow: #ffeb3b;
|
||||
--orange: #ff9800;
|
||||
--red: #f44336;
|
||||
--blue: #5b9bd5;
|
||||
--purple: #ab47bc;
|
||||
--cyan: #00bcd4;
|
||||
}
|
||||
|
||||
* { margin: 0; padding: 0; box-sizing: border-box; }
|
||||
|
||||
body {
|
||||
font-family: "Microsoft YaHei", "PingFang SC", "Helvetica Neue", sans-serif;
|
||||
background: var(--bg-primary);
|
||||
color: var(--text-primary);
|
||||
overflow: hidden;
|
||||
height: 100vh;
|
||||
}
|
||||
|
||||
/* ===== 网格布局 ===== */
|
||||
#app {
|
||||
display: grid;
|
||||
grid-template-columns: 1fr 1fr 1fr;
|
||||
grid-template-rows: auto 1fr 1fr;
|
||||
gap: 10px;
|
||||
height: 100vh;
|
||||
padding: 10px;
|
||||
}
|
||||
|
||||
/* ===== 页头 ===== */
|
||||
.header {
|
||||
grid-column: 1 / -1;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
gap: 20px;
|
||||
padding: 10px 0 6px;
|
||||
border-bottom: 2px solid var(--border);
|
||||
position: relative;
|
||||
}
|
||||
|
||||
.header h1 {
|
||||
font-size: 26px;
|
||||
font-weight: 700;
|
||||
letter-spacing: 4px;
|
||||
background: linear-gradient(90deg, var(--cyan), var(--blue), var(--purple));
|
||||
-webkit-background-clip: text;
|
||||
-webkit-text-fill-color: transparent;
|
||||
background-clip: text;
|
||||
}
|
||||
|
||||
.header .subtitle {
|
||||
font-size: 13px;
|
||||
color: var(--text-secondary);
|
||||
position: absolute;
|
||||
right: 16px;
|
||||
bottom: 6px;
|
||||
}
|
||||
|
||||
.header .refresh-dot {
|
||||
width: 8px; height: 8px;
|
||||
border-radius: 50%;
|
||||
background: var(--green);
|
||||
animation: pulse 2s infinite;
|
||||
}
|
||||
|
||||
@keyframes pulse {
|
||||
0%, 100% { opacity: 1; box-shadow: 0 0 4px var(--green); }
|
||||
50% { opacity: 0.4; box-shadow: 0 0 8px var(--green); }
|
||||
}
|
||||
|
||||
/* ===== 面板通用样式 ===== */
|
||||
.panel {
|
||||
background: var(--bg-panel);
|
||||
border: 1px solid var(--border);
|
||||
border-radius: 6px;
|
||||
padding: 10px 12px;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
min-height: 0;
|
||||
position: relative;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.panel-title {
|
||||
font-size: 14px;
|
||||
font-weight: 600;
|
||||
color: var(--text-primary);
|
||||
margin-bottom: 6px;
|
||||
padding-bottom: 6px;
|
||||
border-bottom: 1px solid rgba(26,58,92,0.6);
|
||||
flex-shrink: 0;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
}
|
||||
|
||||
.panel-title .icon { font-size: 16px; }
|
||||
|
||||
.chart {
|
||||
flex: 1;
|
||||
min-height: 0;
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
.empty-state {
|
||||
flex: 1;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
color: var(--text-dim);
|
||||
font-size: 14px;
|
||||
}
|
||||
|
||||
.loading-state {
|
||||
flex: 1;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
color: var(--text-secondary);
|
||||
font-size: 13px;
|
||||
}
|
||||
|
||||
/* ===== Panel 2: 风险仪表盘 ===== */
|
||||
#panel-risk .risk-content {
|
||||
flex: 1;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
gap: 8px;
|
||||
min-height: 0;
|
||||
}
|
||||
|
||||
.risk-circle {
|
||||
width: 90px;
|
||||
height: 90px;
|
||||
border-radius: 50%;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
font-weight: 700;
|
||||
color: #fff;
|
||||
position: relative;
|
||||
box-shadow: 0 0 20px rgba(0,0,0,0.4);
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.risk-circle .risk-label {
|
||||
font-size: 20px;
|
||||
line-height: 1.2;
|
||||
}
|
||||
|
||||
.risk-circle .risk-sub {
|
||||
font-size: 10px;
|
||||
opacity: 0.85;
|
||||
}
|
||||
|
||||
.risk-circle::after {
|
||||
content: '';
|
||||
position: absolute;
|
||||
inset: -6px;
|
||||
border-radius: 50%;
|
||||
border: 2px solid;
|
||||
border-color: inherit;
|
||||
opacity: 0.6;
|
||||
}
|
||||
|
||||
.predictions-row {
|
||||
display: flex;
|
||||
gap: 8px;
|
||||
width: 100%;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.pred-card {
|
||||
flex: 1;
|
||||
background: rgba(255,255,255,0.03);
|
||||
border-left: 3px solid;
|
||||
border-radius: 0 4px 4px 0;
|
||||
padding: 6px 8px;
|
||||
font-size: 11px;
|
||||
}
|
||||
|
||||
.pred-card .pred-scale {
|
||||
color: var(--text-secondary);
|
||||
margin-bottom: 2px;
|
||||
}
|
||||
|
||||
.pred-card .pred-value {
|
||||
font-size: 14px;
|
||||
font-weight: 700;
|
||||
}
|
||||
|
||||
.pred-card .pred-conf {
|
||||
color: var(--text-dim);
|
||||
font-size: 10px;
|
||||
}
|
||||
|
||||
.suggestions-box {
|
||||
width: 100%;
|
||||
background: rgba(255,255,255,0.02);
|
||||
border-radius: 4px;
|
||||
padding: 6px 10px;
|
||||
font-size: 11px;
|
||||
color: var(--text-secondary);
|
||||
flex-shrink: 0;
|
||||
max-height: 90px;
|
||||
overflow-y: auto;
|
||||
}
|
||||
|
||||
.suggestions-box .sug-title {
|
||||
font-weight: 600;
|
||||
color: var(--text-primary);
|
||||
margin-bottom: 3px;
|
||||
font-size: 12px;
|
||||
}
|
||||
|
||||
.suggestions-box li {
|
||||
margin-left: 16px;
|
||||
margin-bottom: 2px;
|
||||
line-height: 1.5;
|
||||
}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
|
||||
<div id="app">
|
||||
<!-- 页头 -->
|
||||
<div class="header">
|
||||
<div class="refresh-dot" id="refresh-dot"></div>
|
||||
<h1>银发群体高温多时间尺度预警与服务优化可视化平台</h1>
|
||||
<span class="subtitle" id="update-time">数据加载中...</span>
|
||||
</div>
|
||||
|
||||
<!-- Panel 1: 双城温度趋势图 -->
|
||||
<div class="panel" id="panel-1">
|
||||
<div class="panel-title"><span class="icon">📈</span> 双城温度 & 体感温度趋势</div>
|
||||
<div class="chart" id="chart-1"></div>
|
||||
<div class="empty-state" id="empty-1" style="display:none">暂无历史数据</div>
|
||||
</div>
|
||||
|
||||
<!-- Panel 2: 当前风险等级 -->
|
||||
<div class="panel" id="panel-risk">
|
||||
<div class="panel-title"><span class="icon">⚠</span> 当前风险等级评估</div>
|
||||
<div class="risk-content" id="risk-content">
|
||||
<div class="risk-circle" id="risk-circle">
|
||||
<span class="risk-label" id="risk-label">--</span>
|
||||
<span class="risk-sub" id="risk-city">焦作</span>
|
||||
</div>
|
||||
<div class="predictions-row" id="predictions-row"></div>
|
||||
<div class="suggestions-box" id="suggestions-box">
|
||||
<div class="sug-title">📋 建议措施</div>
|
||||
<ul id="suggestions-list"><li>加载中...</li></ul>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Panel 3: 老年人口概况 -->
|
||||
<div class="panel" id="panel-3">
|
||||
<div class="panel-title"><span class="icon">👴</span> 老年人口概况</div>
|
||||
<div class="chart" id="chart-3"></div>
|
||||
<div class="empty-state" id="empty-3" style="display:none">暂无人口数据</div>
|
||||
</div>
|
||||
|
||||
<!-- Panel 4: 多时间尺度预警时间线 -->
|
||||
<div class="panel" id="panel-4">
|
||||
<div class="panel-title"><span class="icon">🕐</span> 多时间尺度预警时间线</div>
|
||||
<div class="chart" id="chart-4"></div>
|
||||
<div class="empty-state" id="empty-4" style="display:none">暂无预测数据</div>
|
||||
</div>
|
||||
|
||||
<!-- Panel 5: 温度与健康风险关联 -->
|
||||
<div class="panel" id="panel-5">
|
||||
<div class="panel-title"><span class="icon">📊</span> 温度与健康风险关联（暴露-反应曲线）</div>
|
||||
<div class="chart" id="chart-5"></div>
|
||||
</div>
|
||||
|
||||
<!-- Panel 6: 历年高温事件回顾 -->
|
||||
<div class="panel" id="panel-6">
|
||||
<div class="panel-title"><span class="icon">📅</span> 历年高温事件与热浪天数统计</div>
|
||||
<div class="chart" id="chart-6"></div>
|
||||
<div class="empty-state" id="empty-6" style="display:none">暂无统计数据</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<script>
|
||||
// ===== 全局状态 =====
|
||||
const BASE = '';
|
||||
const RISK_COLORS = ['#00e676', '#ffeb3b', '#ff9800', '#f44336'];
|
||||
const RISK_LABELS = ['低风险', '中风险', '高风险', '严重风险'];
|
||||
const TIME_SCALE_LABELS = { short: '短期(1-3天)', medium: '中期(7天)', long: '长期(30天)' };
|
||||
const TIME_SCALE_ORDER = ['short', 'medium', 'long'];
|
||||
|
||||
// ECharts 实例池
|
||||
const charts = {};
|
||||
|
||||
// ===== 工具函数 =====
|
||||
function darkAxisLine() {
|
||||
return { lineStyle: { color: '#1a3a5c' } };
|
||||
}
|
||||
|
||||
function darkSplitLine() {
|
||||
return { lineStyle: { color: 'rgba(26,58,92,0.3)', type: 'dashed' } };
|
||||
}
|
||||
|
||||
function darkTextStyle() {
|
||||
return { color: '#8899aa', fontSize: 11 };
|
||||
}
|
||||
|
||||
function panelTitleStyle(text) {
|
||||
return {
|
||||
text: text,
|
||||
textStyle: { color: '#c0ccd8', fontSize: 13, fontWeight: 600 },
|
||||
left: 'center', top: 0
|
||||
};
|
||||
}
|
||||
|
||||
// ===== 数据获取 =====
|
||||
async function fetchAllData() {
|
||||
try {
|
||||
const [predictRes, historyRes, statsRes] = await Promise.all([
|
||||
fetch(BASE + '/api/predict').then(r => r.ok ? r.json() : null),
|
||||
fetch(BASE + '/api/history').then(r => r.ok ? r.json() : null),
|
||||
fetch(BASE + '/api/stats').then(r => r.ok ? r.json() : null)
|
||||
]);
|
||||
|
||||
return {
|
||||
predict: predictRes,
|
||||
history: historyRes,
|
||||
stats: statsRes
|
||||
};
|
||||
} catch (e) {
|
||||
console.error('数据获取失败:', e);
|
||||
return { predict: null, history: null, stats: null };
|
||||
}
|
||||
}
|
||||
|
||||
// ===== Panel 1: 双城温度趋势图 (双Y轴折线图) =====
|
||||
function renderPanel1(historyData) {
|
||||
const dom = document.getElementById('chart-1');
|
||||
const emptyEl = document.getElementById('empty-1');
|
||||
if (!historyData || !historyData.dates || historyData.dates.length === 0) {
|
||||
dom.style.display = 'none';
|
||||
emptyEl.style.display = 'flex';
|
||||
return;
|
||||
}
|
||||
dom.style.display = '';
|
||||
emptyEl.style.display = 'none';
|
||||
|
||||
if (!charts.p1) {
|
||||
charts.p1 = echarts.init(dom);
|
||||
}
|
||||
|
||||
const opt = {
|
||||
tooltip: {
|
||||
trigger: 'axis',
|
||||
backgroundColor: 'rgba(13,31,60,0.95)',
|
||||
borderColor: '#1a3a5c',
|
||||
textStyle: { color: '#e0e6f0', fontSize: 12 }
|
||||
},
|
||||
legend: {
|
||||
data: ['平均温度', '体感温度', '风险等级'],
|
||||
bottom: 0,
|
||||
textStyle: { color: '#8899aa', fontSize: 10 },
|
||||
itemWidth: 14, itemHeight: 8
|
||||
},
|
||||
grid: { left: 45, right: 55, top: 12, bottom: 28 },
|
||||
xAxis: {
|
||||
type: 'category',
|
||||
data: historyData.dates,
|
||||
axisLine: darkAxisLine(),
|
||||
axisTick: { show: false },
|
||||
axisLabel: { color: '#667788', fontSize: 9, rotate: 30,
|
||||
formatter: v => v.slice(5) },
|
||||
splitLine: { show: false }
|
||||
},
|
||||
yAxis: [
|
||||
{
|
||||
type: 'value', name: '°C',
|
||||
nameTextStyle: { color: '#8899aa', fontSize: 10 },
|
||||
axisLine: darkAxisLine(),
|
||||
splitLine: darkSplitLine(),
|
||||
axisLabel: { color: '#8899aa', fontSize: 10 }
|
||||
},
|
||||
{
|
||||
type: 'value', name: '风险',
|
||||
min: 0, max: 3, interval: 1,
|
||||
nameTextStyle: { color: '#ab47bc', fontSize: 10 },
|
||||
axisLine: { lineStyle: { color: '#ab47bc' } },
|
||||
splitLine: { show: false },
|
||||
axisLabel: {
|
||||
color: '#ab47bc', fontSize: 10,
|
||||
formatter: v => RISK_LABELS[v] || ''
|
||||
}
|
||||
}
|
||||
],
|
||||
series: [
|
||||
{
|
||||
name: '平均温度', type: 'line', yAxisIndex: 0,
|
||||
data: historyData.temp_mean,
|
||||
smooth: true, symbol: 'none',
|
||||
lineStyle: { color: '#ff9800', width: 2 },
|
||||
itemStyle: { color: '#ff9800' }
|
||||
},
|
||||
{
|
||||
name: '体感温度', type: 'line', yAxisIndex: 0,
|
||||
data: historyData.heat_index,
|
||||
smooth: true, symbol: 'none',
|
||||
lineStyle: { color: '#f44336', width: 2 },
|
||||
itemStyle: { color: '#f44336' }
|
||||
},
|
||||
{
|
||||
name: '风险等级', type: 'line', yAxisIndex: 1,
|
||||
data: historyData.risk_label,
|
||||
smooth: true, symbol: 'none',
|
||||
lineStyle: { color: '#ab47bc', width: 1.5, type: 'dotted' },
|
||||
itemStyle: { color: '#ab47bc' }
|
||||
}
|
||||
]
|
||||
};
|
||||
charts.p1.setOption(opt, true);
|
||||
}
|
||||
|
||||
// ===== Panel 2: 当前风险等级 (HTML 仪表盘) =====
|
||||
function renderPanel2(predictData) {
|
||||
const circle = document.getElementById('risk-circle');
|
||||
const label = document.getElementById('risk-label');
|
||||
const city = document.getElementById('risk-city');
|
||||
const predRow = document.getElementById('predictions-row');
|
||||
const sugList = document.getElementById('suggestions-list');
|
||||
|
||||
if (!predictData || !predictData.predictions) {
|
||||
circle.style.background = '#555';
|
||||
circle.style.borderColor = '#555';
|
||||
label.textContent = '--';
|
||||
city.textContent = '无数据';
|
||||
predRow.innerHTML = '<span style="color:#667;font-size:11px;text-align:center;width:100%">暂无预测数据</span>';
|
||||
sugList.innerHTML = '<li>暂无建议</li>';
|
||||
return;
|
||||
}
|
||||
|
||||
const preds = predictData.predictions;
|
||||
// 使用短期预测作为当前风险
|
||||
const current = preds.short;
|
||||
const color = RISK_COLORS[current.level] || '#555';
|
||||
|
||||
circle.style.background = color;
|
||||
circle.style.borderColor = color;
|
||||
label.textContent = current.label;
|
||||
city.textContent = predictData.city || '焦作';
|
||||
|
||||
// 三列预测卡片
|
||||
const cardColors = { short: '#5b9bd5', medium: '#ff9800', long: '#ab47bc' };
|
||||
predRow.innerHTML = TIME_SCALE_ORDER.map(key => {
|
||||
const p = preds[key];
|
||||
const c = RISK_COLORS[p.level] || '#555';
|
||||
return '<div class="pred-card" style="border-left-color:' + c + '">' +
|
||||
'<div class="pred-scale">' + TIME_SCALE_LABELS[key] + '</div>' +
|
||||
'<div class="pred-value" style="color:' + c + '">' + p.label + '</div>' +
|
||||
'<div class="pred-conf">置信度: ' + (p.confidence * 100).toFixed(1) + '%</div>' +
|
||||
'</div>';
|
||||
}).join('');
|
||||
|
||||
// 建议列表
|
||||
const suggestions = current.suggestions || ['暂无建议'];
|
||||
sugList.innerHTML = suggestions.map(s => '<li>' + s + '</li>').join('');
|
||||
}
|
||||
|
||||
// ===== Panel 3: 老年人口概况 (环形饼图) =====
|
||||
function renderPanel3(statsData) {
|
||||
const dom = document.getElementById('chart-3');
|
||||
const emptyEl = document.getElementById('empty-3');
|
||||
const agingRate = statsData ? statsData.aging_rate : null;
|
||||
if (!agingRate) {
|
||||
dom.style.display = 'none';
|
||||
emptyEl.style.display = 'flex';
|
||||
return;
|
||||
}
|
||||
dom.style.display = '';
|
||||
emptyEl.style.display = 'none';
|
||||
|
||||
if (!charts.p3) {
|
||||
charts.p3 = echarts.init(dom);
|
||||
}
|
||||
|
||||
const jzRate = agingRate.jiaozuo || 12.8;
|
||||
const zzRate = agingRate.zhengzhou || 11.6;
|
||||
const jzOther = 100 - jzRate;
|
||||
const zzOther = 100 - zzRate;
|
||||
|
||||
const opt = {
|
||||
tooltip: {
|
||||
trigger: 'item',
|
||||
backgroundColor: 'rgba(13,31,60,0.95)',
|
||||
borderColor: '#1a3a5c',
|
||||
textStyle: { color: '#e0e6f0', fontSize: 12 },
|
||||
formatter: '{b}: {c}%'
|
||||
},
|
||||
series: [
|
||||
{
|
||||
name: '焦作', type: 'pie',
|
||||
radius: ['45%', '65%'],
|
||||
center: ['25%', '55%'],
|
||||
label: {
|
||||
position: 'center',
|
||||
formatter: '焦作\n{c}%',
|
||||
fontSize: 13, fontWeight: 700,
|
||||
color: '#e0e6f0'
|
||||
},
|
||||
emphasis: { label: { fontSize: 16 } },
|
||||
data: [
|
||||
{ value: jzRate, name: '老龄化率', itemStyle: { color: '#5b9bd5' } },
|
||||
{ value: jzOther, name: '其他', itemStyle: { color: '#1a3a5c' } }
|
||||
]
|
||||
},
|
||||
{
|
||||
name: '郑州', type: 'pie',
|
||||
radius: ['45%', '65%'],
|
||||
center: ['75%', '55%'],
|
||||
label: {
|
||||
position: 'center',
|
||||
formatter: '郑州\n{c}%',
|
||||
fontSize: 13, fontWeight: 700,
|
||||
color: '#e0e6f0'
|
||||
},
|
||||
emphasis: { label: { fontSize: 16 } },
|
||||
data: [
|
||||
{ value: zzRate, name: '老龄化率', itemStyle: { color: '#ab47bc' } },
|
||||
{ value: zzOther, name: '其他', itemStyle: { color: '#1a3a5c' } }
|
||||
]
|
||||
}
|
||||
],
|
||||
graphic: [
|
||||
{ type: 'text', left: '16%', top: '10%',
|
||||
style: { text: '焦作', fill: '#8899aa', fontSize: 11 } },
|
||||
{ type: 'text', left: '66%', top: '10%',
|
||||
style: { text: '郑州', fill: '#8899aa', fontSize: 11 } },
|
||||
{ type: 'text', left: 'center', top: '88%',
|
||||
style: { text: '65岁及以上人口占比', fill: '#556677', fontSize: 10 } }
|
||||
]
|
||||
};
|
||||
charts.p3.setOption(opt, true);
|
||||
}
|
||||
|
||||
// ===== Panel 4: 多时间尺度预警时间线 (横向条形图) =====
|
||||
function renderPanel4(predictData) {
|
||||
const dom = document.getElementById('chart-4');
|
||||
const emptyEl = document.getElementById('empty-4');
|
||||
if (!predictData || !predictData.predictions) {
|
||||
dom.style.display = 'none';
|
||||
emptyEl.style.display = 'flex';
|
||||
return;
|
||||
}
|
||||
dom.style.display = '';
|
||||
emptyEl.style.display = 'none';
|
||||
|
||||
if (!charts.p4) {
|
||||
charts.p4 = echarts.init(dom);
|
||||
}
|
||||
|
||||
const preds = predictData.predictions;
|
||||
const yLabels = TIME_SCALE_ORDER.map(k => TIME_SCALE_LABELS[k]).reverse();
|
||||
const levels = TIME_SCALE_ORDER.map(k => preds[k].level).reverse();
|
||||
const confs = TIME_SCALE_ORDER.map(k => preds[k].confidence).reverse();
|
||||
const colors = levels.map(l => RISK_COLORS[l]);
|
||||
|
||||
const opt = {
|
||||
tooltip: {
|
||||
trigger: 'axis',
|
||||
backgroundColor: 'rgba(13,31,60,0.95)',
|
||||
borderColor: '#1a3a5c',
|
||||
textStyle: { color: '#e0e6f0', fontSize: 12 },
|
||||
formatter: function(params) {
|
||||
const p = params[0];
|
||||
const idx = p.dataIndex;
|
||||
return '<b>' + yLabels[idx] + '</b><br/>' +
|
||||
'风险等级: ' + RISK_LABELS[levels[idx]] + '<br/>' +
|
||||
'等级值: ' + levels[idx] + ' / 3<br/>' +
|
||||
'置信度: ' + (confs[idx] * 100).toFixed(1) + '%';
|
||||
}
|
||||
},
|
||||
grid: { left: 110, right: 40, top: 16, bottom: 16 },
|
||||
xAxis: {
|
||||
type: 'value', name: '风险等级',
|
||||
min: 0, max: 3, interval: 1,
|
||||
nameTextStyle: { color: '#8899aa', fontSize: 10 },
|
||||
axisLine: darkAxisLine(),
|
||||
splitLine: darkSplitLine(),
|
||||
axisLabel: {
|
||||
color: '#8899aa', fontSize: 10,
|
||||
formatter: v => RISK_LABELS[v] || ''
|
||||
}
|
||||
},
|
||||
yAxis: {
|
||||
type: 'category',
|
||||
data: yLabels,
|
||||
axisLine: { lineStyle: { color: '#1a3a5c' } },
|
||||
axisTick: { show: false },
|
||||
axisLabel: { color: '#c0ccd8', fontSize: 12, fontWeight: 600 }
|
||||
},
|
||||
series: [{
|
||||
type: 'bar',
|
||||
data: levels.map((l, i) => ({
|
||||
value: l,
|
||||
itemStyle: {
|
||||
color: new echarts.graphic.LinearGradient(0, 0, 1, 0, [
|
||||
{ offset: 0, color: colors[i] },
|
||||
{ offset: 1, color: colors[i] + '88' }
|
||||
]),
|
||||
borderRadius: [0, 4, 4, 0]
|
||||
}
|
||||
})),
|
||||
barWidth: 22,
|
||||
label: {
|
||||
show: true, position: 'right',
|
||||
color: '#c0ccd8', fontSize: 11,
|
||||
formatter: function(p) {
|
||||
return RISK_LABELS[levels[p.dataIndex]];
|
||||
}
|
||||
},
|
||||
emphasis: {
|
||||
itemStyle: { shadowBlur: 10, shadowColor: 'rgba(0,0,0,0.4)' }
|
||||
}
|
||||
}]
|
||||
};
|
||||
charts.p4.setOption(opt, true);
|
||||
}
|
||||
|
||||
// ===== Panel 5: 温度与健康风险关联 -- 暴露-反应曲线 (硬编码文献数据) =====
|
||||
function renderPanel5() {
|
||||
const dom = document.getElementById('chart-5');
|
||||
if (!charts.p5) {
|
||||
charts.p5 = echarts.init(dom);
|
||||
}
|
||||
|
||||
// 文献中的典型温度-死亡风险暴露反应曲线 (J型曲线)
|
||||
// 参考: 中国多城市温度-死亡率研究, 以25°C为最低风险参考温度 (RR=1.0)
|
||||
var temps = [15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40];
|
||||
var rr = [1.08, 1.06, 1.04, 1.03, 1.01, 1.00, 0.99, 0.98, 0.98, 0.99, 1.00, 1.00, 1.01, 1.03, 1.06, 1.10, 1.15, 1.22, 1.30, 1.40, 1.52, 1.66, 1.82, 2.00, 2.20, 2.42];
|
||||
|
||||
// 构建高于参考线的面积填充数据
|
||||
var areaData = temps.map(function(t, i) {
|
||||
return [t, Math.max(rr[i], 1.0)];
|
||||
});
|
||||
|
||||
var opt = {
|
||||
tooltip: {
|
||||
trigger: 'axis',
|
||||
backgroundColor: 'rgba(13,31,60,0.95)',
|
||||
borderColor: '#1a3a5c',
|
||||
textStyle: { color: '#e0e6f0', fontSize: 12 },
|
||||
formatter: function(params) {
|
||||
var p = params[0];
|
||||
return '温度: <b>' + p.axisValue + '°C</b><br/>' +
|
||||
'相对风险(RR): <b style="color:' + (p.value > 1.0 ? '#f44336' : '#00e676') + '">' +
|
||||
p.value.toFixed(2) + '</b>';
|
||||
}
|
||||
},
|
||||
grid: { left: 50, right: 30, top: 16, bottom: 24 },
|
||||
xAxis: {
|
||||
type: 'category',
|
||||
data: temps,
|
||||
name: '温度 (°C)',
|
||||
nameTextStyle: { color: '#8899aa', fontSize: 10 },
|
||||
axisLine: darkAxisLine(),
|
||||
axisLabel: { color: '#8899aa', fontSize: 9, interval: 2 },
|
||||
splitLine: { show: false }
|
||||
},
|
||||
yAxis: {
|
||||
type: 'value',
|
||||
name: '相对风险 (RR)',
|
||||
nameTextStyle: { color: '#8899aa', fontSize: 10 },
|
||||
axisLine: darkAxisLine(),
|
||||
splitLine: darkSplitLine(),
|
||||
axisLabel: { color: '#8899aa', fontSize: 10 },
|
||||
min: 0.9
|
||||
},
|
||||
series: [
|
||||
{
|
||||
name: '风险面积', type: 'line',
|
||||
data: areaData,
|
||||
smooth: true, symbol: 'none',
|
||||
lineStyle: { opacity: 0 },
|
||||
areaStyle: { color: 'rgba(244,67,54,0.15)' },
|
||||
silent: true
|
||||
},
|
||||
{
|
||||
name: '相对风险', type: 'line',
|
||||
data: rr,
|
||||
smooth: true, symbol: 'none',
|
||||
lineStyle: { color: '#ff9800', width: 2.5 },
|
||||
itemStyle: { color: '#ff9800' },
|
||||
markLine: {
|
||||
silent: true,
|
||||
symbol: 'none',
|
||||
lineStyle: { color: '#00e676', type: 'dashed', width: 1.5 },
|
||||
label: { color: '#00e676', fontSize: 10, formatter: 'RR=1.0 参考线' },
|
||||
data: [{ yAxis: 1.0 }]
|
||||
},
|
||||
markArea: {
|
||||
silent: true,
|
||||
data: [
|
||||
[
|
||||
{ xAxis: '30', itemStyle: { color: 'rgba(244,67,54,0.06)' } },
|
||||
{ xAxis: '40' }
|
||||
]
|
||||
]
|
||||
}
|
||||
}
|
||||
]
|
||||
};
|
||||
charts.p5.setOption(opt, true);
|
||||
}
|
||||
|
||||
// ===== Panel 6: 历年高温事件与热浪天数统计 (柱状+双折线组合图) =====
|
||||
function renderPanel6(statsData) {
|
||||
var dom = document.getElementById('chart-6');
|
||||
var emptyEl = document.getElementById('empty-6');
|
||||
var annual = statsData ? statsData.annual : null;
|
||||
if (!annual || !annual.years || annual.years.length === 0) {
|
||||
dom.style.display = 'none';
|
||||
emptyEl.style.display = 'flex';
|
||||
return;
|
||||
}
|
||||
dom.style.display = '';
|
||||
emptyEl.style.display = 'none';
|
||||
|
||||
if (!charts.p6) {
|
||||
charts.p6 = echarts.init(dom);
|
||||
}
|
||||
|
||||
var opt = {
|
||||
tooltip: {
|
||||
trigger: 'axis',
|
||||
backgroundColor: 'rgba(13,31,60,0.95)',
|
||||
borderColor: '#1a3a5c',
|
||||
textStyle: { color: '#e0e6f0', fontSize: 12 }
|
||||
},
|
||||
legend: {
|
||||
data: ['热浪天数', '平均温度', '最高温度'],
|
||||
bottom: 0,
|
||||
textStyle: { color: '#8899aa', fontSize: 10 },
|
||||
itemWidth: 14, itemHeight: 8
|
||||
},
|
||||
grid: { left: 45, right: 55, top: 12, bottom: 28 },
|
||||
xAxis: {
|
||||
type: 'category',
|
||||
data: annual.years.map(String),
|
||||
axisLine: darkAxisLine(),
|
||||
axisLabel: { color: '#8899aa', fontSize: 10 },
|
||||
splitLine: { show: false }
|
||||
},
|
||||
yAxis: [
|
||||
{
|
||||
type: 'value', name: '°C',
|
||||
nameTextStyle: { color: '#8899aa', fontSize: 10 },
|
||||
axisLine: darkAxisLine(),
|
||||
splitLine: darkSplitLine(),
|
||||
axisLabel: { color: '#8899aa', fontSize: 10 }
|
||||
},
|
||||
{
|
||||
type: 'value', name: '天',
|
||||
nameTextStyle: { color: '#ffeb3b', fontSize: 10 },
|
||||
axisLine: { lineStyle: { color: '#ffeb3b' } },
|
||||
splitLine: { show: false },
|
||||
axisLabel: { color: '#ffeb3b', fontSize: 10 }
|
||||
}
|
||||
],
|
||||
series: [
|
||||
{
|
||||
name: '热浪天数', type: 'bar', yAxisIndex: 1,
|
||||
data: annual.heatwave_days,
|
||||
barWidth: '40%',
|
||||
itemStyle: {
|
||||
color: new echarts.graphic.LinearGradient(0, 0, 0, 1, [
|
||||
{ offset: 0, color: '#ffeb3b' },
|
||||
{ offset: 1, color: '#ff980088' }
|
||||
])
|
||||
}
|
||||
},
|
||||
{
|
||||
name: '平均温度', type: 'line', yAxisIndex: 0,
|
||||
data: annual.avg_temp,
|
||||
smooth: true, symbol: 'circle', symbolSize: 5,
|
||||
lineStyle: { color: '#5b9bd5', width: 2 },
|
||||
itemStyle: { color: '#5b9bd5' }
|
||||
},
|
||||
{
|
||||
name: '最高温度', type: 'line', yAxisIndex: 0,
|
||||
data: annual.max_temp,
|
||||
smooth: true, symbol: 'diamond', symbolSize: 6,
|
||||
lineStyle: { color: '#f44336', width: 2 },
|
||||
itemStyle: { color: '#f44336' }
|
||||
}
|
||||
]
|
||||
};
|
||||
charts.p6.setOption(opt, true);
|
||||
}
|
||||
|
||||
// ===== 更新时间戳 =====
|
||||
function updateTimestamp() {
|
||||
var now = new Date();
|
||||
var str = now.getFullYear() + '-' +
|
||||
String(now.getMonth() + 1).padStart(2, '0') + '-' +
|
||||
String(now.getDate()).padStart(2, '0') + ' ' +
|
||||
String(now.getHours()).padStart(2, '0') + ':' +
|
||||
String(now.getMinutes()).padStart(2, '0') + ':' +
|
||||
String(now.getSeconds()).padStart(2, '0');
|
||||
document.getElementById('update-time').textContent = '数据更新: ' + str;
|
||||
}
|
||||
|
||||
// ===== 初始化所有图表 =====
|
||||
async function initCharts() {
|
||||
var data = await fetchAllData();
|
||||
|
||||
renderPanel1(data.history);
|
||||
renderPanel2(data.predict);
|
||||
renderPanel3(data.stats);
|
||||
renderPanel4(data.predict);
|
||||
renderPanel5();
|
||||
renderPanel6(data.stats);
|
||||
|
||||
updateTimestamp();
|
||||
}
|
||||
|
||||
// ===== 响应式处理 =====
|
||||
function resizeAllCharts() {
|
||||
Object.values(charts).forEach(function(c) {
|
||||
if (c && !c.isDisposed()) {
|
||||
c.resize();
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
// ===== 入口 =====
|
||||
document.addEventListener('DOMContentLoaded', function() {
|
||||
initCharts();
|
||||
|
||||
// 每30分钟自动刷新
|
||||
setInterval(initCharts, 30 * 60 * 1000);
|
||||
|
||||
// 窗口缩放时重绘所有图表
|
||||
window.addEventListener('resize', function() {
|
||||
clearTimeout(window._resizeTimer);
|
||||
window._resizeTimer = setTimeout(resizeAllCharts, 200);
|
||||
});
|
||||
});
|
||||
</script>
|
||||
|
||||
</body>
|
||||
</html>
|
||||
Reference in New Issue
Block a user