fix(data_processor.py): 修复气压 NaN 值未填充导致验证失败的问题

calculate_pressure() 函数：
- 原逻辑：只在 max_samples 截断模式下用平均值填充剩余行的 NaN 气压值，
  正常全量计算时若某些高度档位 API 失败（返回 None），NaN 直接传播至数据验证器，
  触发 'Column pressure contains NaN values' ValueError，导致任务失败
- 修复：将 NaN 填充逻辑从条件分支中提取为通用处理，
  任何时候只要有有效气压值就用其平均值填充所有 NaN 行，
  并在统计信息中明确报告填充行数

影响范围：消除因 Open-Meteo API 波动导致的整任务失败

src/gasflux/data_processor.py

@@ -544,17 +544,17 @@ def calculate_pressure(df, max_samples=None, height_tolerance=10.0, height_bin_s
     df['pressure'] = None  # 初始化
     df.loc[sample_df.index, 'pressure'] = pressures
 
-    # 对于未计算的行，使用插值或平均值填充
-    if max_samples is not None and len(df) > max_samples:
-        # 只计算了部分行，用平均值填充其余行
-        valid_pressures_for_mean = [p for p in pressures if p is not None]
-        if valid_pressures_for_mean:
-            mean_pressure = sum(valid_pressures_for_mean) / len(valid_pressures_for_mean)
-            df['pressure'] = df['pressure'].fillna(mean_pressure)
-            print(f"使用平均气压填充其余 {len(df) - max_samples} 行: {mean_pressure:.1f} hPa")
+    # 统计并填充缺失气压值：用已有有效值的平均值填充所有NaN
+    valid_pressures = [p for p in pressures if p is not None]
+    nan_count_before = df['pressure'].isna().sum()
+
+    if valid_pressures:
+        mean_pressure = sum(valid_pressures) / len(valid_pressures)
+        df['pressure'] = df['pressure'].fillna(mean_pressure)
+        if nan_count_before > 0:
+            print(f"使用平均气压 {mean_pressure:.1f} hPa 填充了 {nan_count_before} 行缺失气压值")
 
     # 统计信息
-    valid_pressures = [p for p in pressures if p is not None]
     if valid_pressures:
         avg_pressure = sum(valid_pressures) / len(valid_pressures)
         print(f"成功计算 {len(valid_pressures)}/{actual_samples} 个气压值，平均值: {avg_pressure:.1f} hPa")