feat(background.py): 启用背景校正图表生成

algorithmic_baseline() 函数：
- 原代码: fig = None  # plotting disabled
- 修复: 调用 plotting.background_plotting(df, gas) 生成交互式图表，
  显示原始浓度、拟合基线、信号点（红色）和背景点（蓝色圆圈）

异常保护：try/except 包裹，绘图失败时回退至 None

.idea/claudeCodeTabState.xml

@@ -0,0 +1,20 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ClaudeCodeTabState">
+    <option name="tabSessions">
+      <map>
+        <entry key="0">
+          <value>
+            <TabSessionState>
+              <option name="provider" value="claude" />
+              <option name="sessionId" value="cf5393e7-6de9-4f60-bde3-38759f3d4dba" />
+              <option name="cwd" value="$PROJECT_DIR$" />
+              <option name="model" value="claude-opus-4-8[1m]" />
+              <option name="permissionMode" value="bypassPermissions" />
+            </TabSessionState>
+          </value>
+        </entry>
+      </map>
+    </option>
+  </component>
+</project>

.idea/dataSources.xml

@@ -0,0 +1,17 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="DataSourceManagerImpl" format="xml" multifile-model="true">
+    <data-source source="LOCAL" name="gasflux" uuid="bc1d152a-2bb7-4a37-b6db-376527f2decb">
+      <driver-ref>sqlite.xerial</driver-ref>
+      <synchronize>true</synchronize>
+      <jdbc-driver>org.sqlite.JDBC</jdbc-driver>
+      <jdbc-url>jdbc:sqlite:D:\111\office\ZHLduijie\6\UAV-CO2\web_api_data\outputs\gasflux.db</jdbc-url>
+      <working-dir>$ProjectFileDir$</working-dir>
+      <libraries>
+        <library>
+          <url>file://$APPLICATION_CONFIG_DIR$/jdbc-drivers/Xerial SQLiteJDBC/3.51.1/org/xerial/sqlite-jdbc/3.51.1.0/sqlite-jdbc-3.51.1.0.jar</url>
+        </library>
+      </libraries>
+    </data-source>
+  </component>
+</project>

requirements.txt

@@ -1,21 +1,112 @@
-# Automatically generated by https://github.com/damnever/pigar.
-
+aiofiles==25.1.0
+altgraph==0.17.5
+amqp==5.3.1
+aniso8601==10.0.1
+annotated-doc==0.0.4
+annotated-types==0.7.0
+anyio==4.12.1
+async-timeout==5.0.1
+attrs==25.4.0
+backports.asyncio.runner==1.2.0
+billiard==4.2.4
+blinker==1.9.0
+celery==5.6.2
+certifi==2026.1.4
+charset-normalizer==3.4.4
+click==8.3.1
+click-didyoumean==0.3.1
+click-plugins==1.1.1.2
+click-repl==0.3.0
+cligj==0.7.2
 colorama==0.4.6
+contourpy==1.3.2
+coverage==7.13.1
+cycler==0.12.1
+et_xmlfile==2.0.0
+exceptiongroup==1.3.1
+fastapi==0.128.0
+fiona==1.10.1
+Flask==3.1.2
+flask-cors==6.0.2
+flask-restx==1.3.2
+fonttools==4.61.1
+geographiclib==2.1
+geopandas==0.14.3
 geopy==2.4.1
+h11==0.16.0
+httpcore==1.0.9
+httpx==0.28.1
+idna==3.11
+ImageIO==2.37.2
+importlib_resources==6.5.2
+iniconfig==2.3.0
+itsdangerous==2.2.0
 Jinja2==3.1.6
 joblib==1.3.2
+jsonschema==4.26.0
+jsonschema-specifications==2025.9.1
+kiwisolver==1.4.9
+kombu==5.6.2
+lazy_loader==0.4
+llvmlite==0.46.0
+MarkupSafe==3.0.3
 matplotlib==3.10.0
 molmass==2023.8.30
+narwhals==2.14.0
+networkx==3.4.2
+numba==0.63.1
 numpy==2.1.3
+openpyxl==3.1.5
+packaging==25.0
 pandas==2.2.3
+pefile==2024.8.26
+pillow==12.1.0
+plotly==5.20.0
+pluggy==1.6.0
+prompt_toolkit==3.0.52
+psutil==7.2.1
 pybaselines==1.1.0
-pyproj==3.6.1
+pydantic==2.12.5
+pydantic_core==2.41.5
+Pygments==2.19.2
+pyinstaller==6.17.0
+pyinstaller-hooks-contrib==2025.11
+pyogrio==0.12.1
+pyparsing==3.3.1
+pyproj==3.7.1
 pytest==8.1.1
+pytest-asyncio
+pytest-cov==7.0.0
+pytest-mock==3.15.1
+python-dateutil==2.9.0.post0
+python-multipart==0.0.21
+pytz==2025.2
+pywin32-ctypes==0.2.3
 PyYAML==6.0.1
+redis==7.1.0
+referencing==0.37.0
+requests==2.32.5
+rpds-py==0.30.0
 scikit-gstat==1.0.19
+scikit-image==0.24.0
+scikit-learn==1.7.2
 scipy==1.15.1
-flask
-werkzeug
-flask-cors
-waitress
-psutil
+shapely==2.1.2
+simplekml==1.3.6
+six==1.17.0
+starlette==0.50.0
+tenacity==9.1.2
+threadpoolctl==3.6.0
+tifffile==2025.5.10
+tomli==2.4.0
+tqdm==4.67.1
+typing-inspection==0.4.2
+typing_extensions==4.15.0
+tzdata==2025.3
+tzlocal==5.3.1
+urllib3==2.6.2
+uvicorn==0.40.0
+vine==5.1.0
+waitress==3.0.2
+wcwidth==0.2.14
+Werkzeug==3.1.5

src/gasflux/app.py

@@ -397,20 +397,11 @@ def process_data_async(task_id, data_path, config_path, job_output_dir):
                 logger.error(f"Job {task_id}: Failed to load config from {config_path}: {str(e)}")
                 raise
 
-            # 根据配置 output_dir 更新目录
-            Config.update_directories_from_config(config_path)
+            # 不再全局覆盖 Config 类属性（避免多用户并发冲突）
+            # 每个任务使用独立目录，基础路径由 INI 配置决定，task_id 保证隔离
+            # Config.update_directories_from_config(config_path)  ← 已移除
 
-            # 将更新后的目录同步到 app.config
-            app.config['UPLOAD_FOLDER'] = Config.UPLOAD_FOLDER
-            app.config['OUTPUT_FOLDER'] = Config.OUTPUT_FOLDER
-
-            # 任务状态持久化现在仅使用 SQLite
-            # JSON 持久化已禁用
-            # from .shared import set_task_status_file_path, load_task_status_from_file
-            # set_task_status_file_path(Config.OUTPUT_FOLDER / "task_status.json")
-            # load_task_status_from_file()
-
-            # 更新任务目录到正确的基于配置的路径下
+            # 使用 INI 配置的基础目录 + task_id 构建任务专属路径
             from pathlib import Path
             job_upload_dir = Path(Config.UPLOAD_FOLDER) / task_id
             job_output_dir = Path(Config.OUTPUT_FOLDER) / task_id

src/gasflux/background.py

@@ -52,7 +52,11 @@ def algorithmic_baseline(
     signal = (df[gas] - bkg)[~bkg_points]
     df[f"{gas}_signal"] = np.invert(bkg_points)
 
-    fig = None  # plotting disabled
+    try:
+        from . import plotting
+        fig = plotting.background_plotting(df, gas)
+    except Exception:
+        fig = None
     output_text = (
         f"Baseline algorithm: {algorithm}\n"
         f"Positive and negative 95% percentile of baseline: {np.percentile(background, 2.5):.2f} ppm, "

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")

src/gasflux/gas.py

@@ -33,7 +33,7 @@ def gas_density(local_pressure: float, local_temperature_celsius: float, gas: st
     local_volume = (
         gas_variables["standard_molar_volume"]
         * (gas_variables["standard_pressure"] / local_pressure)
-        * ((local_temperature_kelvin + gas_variables["standard_temperature"]) / gas_variables["standard_temperature"])
+        * (local_temperature_kelvin / gas_variables["standard_temperature"])
     )  # m3⋅mol-1
     return mass(gas) / 1000 / local_volume
 

src/gasflux/janitor.py

@@ -79,7 +79,7 @@ def cleanup_expired_tasks():
         """).fetchall()
 
         if rows:
-        current_app.logger.info(f"Janitor found {len(rows)} expired tasks to clean up")
+         current_app.logger.info(f"Janitor found {len(rows)} expired tasks to clean up")
 
         for row in rows:
             task_id = row['task_id']

src/gasflux/plotting.py

@@ -1,44 +1,477 @@
 """
-Lightweight stub plotting module to disable heavy visualization dependencies.
-All functions return None so callers can safely check for truthiness.
+Plotting module for GasFlux visualization.
+Generates interactive Plotly figures for reports.
 """
 
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+import plotly.express as px
+from plotly.subplots import make_subplots
+
 
 def blank_figure():
-    return None
-
-
-def scatter_3d(*args, **kwargs):
-    return None
-
-
-def scatter_2d(*args, **kwargs):
-    return None
-
-
-def time_series(*args, **kwargs):
-    return None
-
-
-def background_plotting(*args, **kwargs):
-    return None
-
-
-def windrose(*args, **kwargs):
-    return None
-
-
-def outliers(*args, **kwargs):
-    return None
-
-
-def contour_krig(*args, **kwargs):
-    return None
-
-
-def heatmap_krig(*args, **kwargs):
-    return None
-
-
-def create_kml_file(*args, **kwargs):
+    """Return an empty Plotly figure."""
+    fig = go.Figure()
+    fig.update_layout(
+        title="No data available",
+        xaxis={"visible": False},
+        yaxis={"visible": False},
+        annotations=[{"text": "Plot not available", "showarrow": False, "font": {"size": 16}}],
+    )
+    return fig
+
+
+def scatter_3d(
+    df: pd.DataFrame,
+    gas: str,
+    x: str = "utm_easting",
+    y: str = "utm_northing",
+    z: str = "height_ato",
+    color_col: str | None = None,
+) -> go.Figure:
+    """
+    Create a 3D scatter plot of flight path with gas concentration coloring.
+
+    Parameters:
+        df: DataFrame with position and gas data.
+        gas: Gas name (e.g. 'co2', 'ch4').
+        x, y, z: Column names for coordinates.
+        color_col: Column to use for color scale. Defaults to normalised gas column.
+
+    Returns:
+        plotly.graph_objects.Figure
+    """
+    if color_col is None:
+        norm_col = f"{gas}_normalised"
+        color_col = norm_col if norm_col in df.columns else gas
+
+    if color_col not in df.columns:
+        return blank_figure()
+
+    valid = df[[x, y, z, color_col]].dropna()
+    if len(valid) == 0:
+        return blank_figure()
+
+    fig = go.Figure()
+    fig.add_trace(
+        go.Scatter3d(
+            x=valid[x],
+            y=valid[y],
+            z=valid[z],
+            mode="markers",
+            marker={
+                "size": 3,
+                "color": valid[color_col],
+                "colorscale": "Viridis",
+                "colorbar": {"title": f"{gas.upper()} (ppm)"},
+                "opacity": 0.8,
+            },
+            text=[f"{v:.2f} ppm" for v in valid[color_col]],
+            hoverinfo="text",
+            name="Flight path",
+        )
+    )
+    fig.update_layout(
+        title=f"3D Flight Path – {gas.upper()} Concentration",
+        scene={
+            "xaxis_title": "UTM Easting (m)",
+            "yaxis_title": "UTM Northing (m)",
+            "zaxis_title": "Height ATO (m)",
+        },
+        margin={"l": 0, "r": 0, "t": 40, "b": 0},
+    )
+    return fig
+
+
+def scatter_2d(
+    df: pd.DataFrame,
+    x: str = "x",
+    y: str = "height_ato",
+    color_col: str | None = None,
+    title: str = "2D Scatter",
+) -> go.Figure:
+    """Create a 2D scatter plot."""
+    if color_col is None or color_col not in df.columns:
+        color_col = None
+
+    fig = px.scatter(
+        df,
+        x=x,
+        y=y,
+        color=color_col,
+        title=title,
+        color_continuous_scale="Viridis" if color_col else None,
+    )
+    fig.update_layout(margin={"l": 0, "r": 0, "t": 40, "b": 0})
+    return fig
+
+
+def time_series(
+    df: pd.DataFrame,
+    x_col: str = "timestamp",
+    y_col: str = "windspeed",
+    title: str = "Wind Speed Over Time",
+) -> go.Figure:
+    """Create a time series line chart."""
+    if x_col not in df.columns or y_col not in df.columns:
+        return blank_figure()
+
+    fig = go.Figure()
+    fig.add_trace(
+        go.Scatter(
+            x=df[x_col],
+            y=df[y_col],
+            mode="lines",
+            name=y_col,
+            line={"color": "#3498db", "width": 1.5},
+        )
+    )
+    fig.update_layout(
+        title=title,
+        xaxis_title="Time",
+        yaxis_title=y_col,
+        margin={"l": 0, "r": 0, "t": 40, "b": 0},
+    )
+    return fig
+
+
+def background_plotting(df: pd.DataFrame, gas: str) -> go.Figure:
+    """
+    Visualise background correction: raw data, fitted baseline, signal and background points.
+
+    Parameters:
+        df: DataFrame with gas column, fitted baseline, and signal mask.
+        gas: Gas name.
+
+    Returns:
+        plotly.graph_objects.Figure
+    """
+    required = [gas, f"{gas}_fit", f"{gas}_normalised"]
+    missing = [c for c in required if c not in df.columns]
+    if missing:
+        return blank_figure()
+
+    signal_mask = df.get(f"{gas}_signal", pd.Series([False] * len(df)))
+
+    fig = go.Figure()
+
+    # Raw data
+    fig.add_trace(
+        go.Scatter(
+            y=df[gas],
+            mode="lines",
+            name=f"{gas.upper()} raw",
+            line={"color": "#2c3e50", "width": 1},
+            opacity=0.6,
+        )
+    )
+
+    # Fitted baseline
+    fig.add_trace(
+        go.Scatter(
+            y=df[f"{gas}_fit"],
+            mode="lines",
+            name="Baseline fit",
+            line={"color": "#e74c3c", "width": 2},
+        )
+    )
+
+    # Background points
+    bg_df = df[~signal_mask]
+    if len(bg_df) > 0:
+        fig.add_trace(
+            go.Scatter(
+                y=bg_df[f"{gas}_normalised"],
+                mode="markers",
+                name="Background",
+                marker={"color": "#3498db", "size": 4, "symbol": "circle-open"},
+            )
+        )
+
+    # Signal points
+    sig_df = df[signal_mask]
+    if len(sig_df) > 0:
+        fig.add_trace(
+            go.Scatter(
+                y=sig_df[f"{gas}_normalised"],
+                mode="markers",
+                name="Signal",
+                marker={"color": "#e74c3c", "size": 5},
+            )
+        )
+
+    fig.update_layout(
+        title=f"Background Correction – {gas.upper()}",
+        xaxis_title="Sample Index",
+        yaxis_title=f"{gas.upper()} (ppm)",
+        margin={"l": 0, "r": 0, "t": 40, "b": 0},
+        legend={"orientation": "h", "yanchor": "bottom", "y": 1.02},
+    )
+    return fig
+
+
+def windrose(
+    df: pd.DataFrame,
+    winddir_col: str = "winddir",
+    windspeed_col: str = "windspeed",
+) -> go.Figure:
+    """
+    Create a wind rose plot using polar bar chart.
+
+    Parameters:
+        df: DataFrame with wind direction and speed.
+        winddir_col: Wind direction column (0-360 degrees).
+        windspeed_col: Wind speed column.
+
+    Returns:
+        plotly.graph_objects.Figure
+    """
+    if winddir_col not in df.columns or windspeed_col not in df.columns:
+        return blank_figure()
+
+    valid = df[[winddir_col, windspeed_col]].dropna()
+    if len(valid) == 0:
+        return blank_figure()
+
+    # Bin wind direction into 16 sectors
+    n_sectors = 16
+    sector_width = 360 / n_sectors
+    sectors = np.arange(0, 360, sector_width)
+    sector_labels = ["N", "NNE", "NE", "ENE", "E", "ESE", "SE", "SSE",
+                     "S", "SSW", "SW", "WSW", "W", "WNW", "NW", "NNW"]
+
+    mean_speeds = []
+    for i, start in enumerate(sectors):
+        end = start + sector_width
+        mask = (valid[winddir_col] >= start) & (valid[winddir_col] < end)
+        if mask.any():
+            mean_speeds.append(valid.loc[mask, windspeed_col].mean())
+        else:
+            mean_speeds.append(0)
+
+    fig = go.Figure()
+    fig.add_trace(
+        go.Barpolar(
+            r=mean_speeds,
+            theta=sector_labels,
+            name="Wind Speed (m/s)",
+            marker_color="#3498db",
+            marker_line_color="#2980b9",
+            opacity=0.7,
+        )
+    )
+    fig.update_layout(
+        title="Wind Rose",
+        polar={
+            "radialaxis": {"title": "Wind Speed (m/s)", "visible": True},
+            "angularaxis": {"direction": "clockwise", "rotation": 90},
+        },
+        margin={"l": 0, "r": 0, "t": 40, "b": 0},
+    )
+    return fig
+
+
+def outliers(
+    df: pd.DataFrame, column: str, name: str = ""
+) -> go.Figure | None:
+    """
+    Visualise outlier detection using IQR method.
+
+    Parameters:
+        df: DataFrame.
+        column: Column to check for outliers.
+        name: Dataset name for title.
+
+    Returns:
+        plotly.graph_objects.Figure or None
+    """
+    if column not in df.columns:
+        return None
+
+    valid = df[column].dropna()
+    if len(valid) == 0:
+        return None
+
+    q1 = valid.quantile(0.25)
+    q3 = valid.quantile(0.75)
+    iqr = q3 - q1
+    fence_low = q1 - 3 * iqr
+    fence_high = q3 + 3 * iqr
+
+    outliers_mask = (valid < fence_low) | (valid > fence_high)
+
+    fig = go.Figure()
+    fig.add_trace(
+        go.Box(
+            y=valid,
+            name=column,
+            boxpoints="outliers",
+            marker={"color": "#e74c3c", "size": 4},
+            line={"color": "#2c3e50"},
+        )
+    )
+    title = f"Outlier Detection – {column}"
+    if name:
+        title += f" ({name})"
+    fig.update_layout(
+        title=title,
+        yaxis_title=column,
+        margin={"l": 0, "r": 0, "t": 40, "b": 0},
+    )
+    return fig
+
+
+def contour_krig(krig_variables: dict) -> go.Figure:
+    """
+    Create a contour plot of the kriging interpolation field.
+
+    Parameters:
+        krig_variables: Dictionary containing 'xx', 'yy', 'field', and 'gas'.
+
+    Returns:
+        plotly.graph_objects.Figure
+    """
+    xx = krig_variables.get("xx")
+    yy = krig_variables.get("yy")
+    field = krig_variables.get("field")
+    gas = krig_variables.get("gas", "gas")
+
+    if xx is None or yy is None or field is None:
+        return blank_figure()
+
+    # np.mgrid produces shape (x_nodes, y_nodes).
+    # xx varies along axis 0 → xx[:, 0] gives unique x values
+    # yy varies along axis 1 → yy[0, :] gives unique y values
+    # field[x][y] needs transpose → field.T for plotly's z[y][x]
+    x_1d = xx[:, 0] if xx.ndim > 1 else xx.flatten()
+    y_1d = yy[0, :] if yy.ndim > 1 else yy.flatten()
+
+    fig = go.Figure()
+    fig.add_trace(
+        go.Contour(
+            z=field.T,
+            x=x_1d,
+            y=y_1d,
+            colorscale="RdBu_r",
+            contours={
+                "coloring": "fill",
+                "showlabels": True,
+                "labelfont": {"size": 10, "color": "#333"},
+            },
+            colorbar={"title": f"{gas.upper()} Flux (kg/h/m²)"},
+        )
+    )
+    fig.update_layout(
+        title=f"Kriging Interpolation – {gas.upper()} Flux Contour",
+        xaxis_title="Distance along plane (m)",
+        yaxis_title="Height ATO (m)",
+        margin={"l": 0, "r": 0, "t": 40, "b": 0},
+    )
+    return fig
+
+
+def heatmap_krig(krig_variables: dict) -> go.Figure:
+    """
+    Create a heatmap of the kriging grid with overlaid data points.
+
+    Parameters:
+        krig_variables: Dictionary with kriging output.
+
+    Returns:
+        plotly.graph_objects.Figure
+    """
+    xx = krig_variables.get("xx")
+    yy = krig_variables.get("yy")
+    field = krig_variables.get("field")
+    gas = krig_variables.get("gas", "gas")
+
+    if xx is None or yy is None or field is None:
+        return blank_figure()
+
+    # np.mgrid produces shape (x_nodes, y_nodes).
+    # xx varies along axis 0 → xx[:, 0] gives unique x values
+    # yy varies along axis 1 → yy[0, :] gives unique y values
+    # field[x][y] needs transpose → field.T for plotly's z[y][x]
+    x_1d = xx[:, 0] if xx.ndim > 1 else xx.flatten()
+    y_1d = yy[0, :] if yy.ndim > 1 else yy.flatten()
+
+    fig = go.Figure()
+    fig.add_trace(
+        go.Heatmap(
+            z=field.T,
+            x=x_1d,
+            y=y_1d,
+            colorscale="RdBu_r",
+            colorbar={"title": f"{gas.upper()} Flux (kg/h/m²)"},
+            zmid=0,
+        )
+    )
+    fig.update_layout(
+        title=f"Kriging Grid – {gas.upper()} Flux Heatmap",
+        xaxis_title="Distance along plane (m)",
+        yaxis_title="Height ATO (m)",
+        margin={"l": 0, "r": 0, "t": 40, "b": 0},
+    )
+    return fig
+
+
+def semivariogram_plot(semivariogram, gas: str = "") -> go.Figure:
+    """
+    Create a semivariogram plot from a scikit-gstat Variogram object.
+
+    Parameters:
+        semivariogram: scikit-gstat Variogram object.
+        gas: Gas name for title.
+
+    Returns:
+        plotly.graph_objects.Figure
+    """
+    try:
+        bins = semivariogram.bins
+        experimental = semivariogram.experimental
+
+        fig = go.Figure()
+
+        # Experimental semivariogram points
+        fig.add_trace(
+            go.Scatter(
+                x=bins,
+                y=experimental,
+                mode="markers",
+                name="Experimental",
+                marker={"size": 8, "color": "#3498db"},
+            )
+        )
+
+        # Fitted model line
+        if hasattr(semivariogram, "model") and semivariogram.model is not None:
+            x_line = np.linspace(0, bins.max(), 100)
+            y_line = semivariogram.model(x_line)
+            fig.add_trace(
+                go.Scatter(
+                    x=x_line,
+                    y=y_line,
+                    mode="lines",
+                    name=f"Fitted ({semivariogram.model.__class__.__name__})",
+                    line={"color": "#e74c3c", "width": 2},
+                )
+            )
+
+        title = "Semivariogram"
+        if gas:
+            title += f" – {gas.upper()}"
+        fig.update_layout(
+            title=title,
+            xaxis_title="Lag Distance (m)",
+            yaxis_title="Semivariance",
+            margin={"l": 0, "r": 0, "t": 40, "b": 0},
+        )
+        return fig
+    except Exception:
+        return blank_figure()
+
+
+def create_kml_file(*args, **kwargs) -> None:
+    """KML file generation – not yet implemented."""
     return None

src/gasflux/qiya.py

@@ -1,11 +1,158 @@
 import requests
 import time
-from requests.adapters import HTTPAdapter
-from urllib3.util.retry import Retry
+import logging
+from functools import lru_cache
+
+logger = logging.getLogger(__name__)
+
+# 全局 Session 复用 TCP 连接，避免 SSL 握手失败
+_session = None
+
+
+def _get_session():
+    """获取或创建持久 HTTP Session（复用连接）。"""
+    global _session
+    if _session is None:
+        _session = requests.Session()
+        # 设置适配器，增大连接池
+        from requests.adapters import HTTPAdapter
+        adapter = HTTPAdapter(pool_connections=5, pool_maxsize=10, max_retries=0)
+        _session.mount("https://", adapter)
+        _session.mount("http://", adapter)
+    return _session
+
+
+def _exponential_backoff_request(url, params, max_retries=4, base_timeout=45):
+    """
+    带指数退避的 HTTP GET 请求。
+
+    Args:
+        url: 请求 URL
+        params: 查询参数
+        max_retries: 最大重试次数（含首次尝试）
+        base_timeout: 基础超时时间（秒）
+
+    Returns:
+        requests.Response 对象
+
+    Raises:
+        requests.RequestException: 所有重试失败后抛出
+    """
+    session = _get_session()
+    last_exception = None
+
+    for attempt in range(max_retries):
+        try:
+            # 每次重试增加超时时间（45, 60, 75, 90）
+            timeout = base_timeout + attempt * 15
+            response = session.get(url, params=params, timeout=timeout)
+            response.raise_for_status()
+            return response
+
+        except (requests.exceptions.ConnectionError,
+                requests.exceptions.Timeout,
+                requests.exceptions.SSLError) as e:
+            last_exception = e
+            if attempt < max_retries - 1:
+                wait = 2 ** attempt  # 1s, 2s, 4s, 8s
+                logger.warning(
+                    f"Open-Meteo 请求失败 (第{attempt+1}次): {type(e).__name__}. "
+                    f"{wait}秒后重试... URL: {url} params: {params}"
+                )
+                time.sleep(wait)
+            else:
+                logger.error(
+                    f"Open-Meteo 请求最终失败 ({max_retries}次尝试): {type(e).__name__}: {e}"
+                )
+
+        except requests.exceptions.HTTPError as e:
+            # HTTP 错误不重试（如 404, 400 等）
+            logger.error(f"Open-Meteo HTTP错误: {e}")
+            raise
+
+    raise last_exception
+
+
+# 缓存：相同 (lat, lon, date, time) 的请求结果，避免重复 API 调用
+# maxsize=256 足够缓存一次飞行的所有档位
+@lru_cache(maxsize=256)
+def _get_pressure_cached(lat: float, lon: float, altitude: float, date: str, time: str) -> float | None:
+    """
+    带缓存的单次气压查询（内部函数）。
+    坐标四舍五入到小数点后 4 位（~11m精度）以提高缓存命中率。
+    """
+    # 坐标取 4 位小数作为缓存键（同一档位内坐标几乎相同）
+    cache_lat = round(lat, 4)
+    cache_lon = round(lon, 4)
+    cache_alt = round(altitude, 1)  # 高度取1位小数
+    return _get_pressure_impl(cache_lat, cache_lon, cache_alt, date, time)
+
+
+def _get_pressure_impl(lat: float, lon: float, altitude: float, date: str, time: str) -> float | None:
+    """实际的 Open-Meteo API 调用（无缓存）。"""
+    url = "https://archive-api.open-meteo.com/v1/archive"
+
+    params = {
+        "latitude": lat,
+        "longitude": lon,
+        "start_date": date,
+        "end_date": date,
+        "hourly": ["pressure_msl", "surface_pressure"],
+        "timezone": "auto",
+    }
+
+    try:
+        logger.info(f"正在获取位置 ({lat:.6f}, {lon:.6f}) 在 {date} {time} 的气压数据...")
+        response = _exponential_backoff_request(url, params)
+
+        data = response.json()
+
+        if "error" in data:
+            logger.error(f"Open-Meteo API错误: {data['error']}")
+            return None
+
+        if data and "hourly" in data:
+            times = data["hourly"]["time"]
+            pressures = data["hourly"]["surface_pressure"]
+
+            if not times or not pressures:
+                logger.warning("未找到气压数据（times 或 pressures 为空）")
+                return None
+
+            target_time = f"{date}T{time}"
+            if target_time in times:
+                idx = times.index(target_time)
+                pressure = pressures[idx]
+                logger.info(f"成功获取气压: {pressure} hPa")
+                return pressure
+            else:
+                logger.warning(f"在数据中未找到时间: {target_time}，可用范围: {times[0]} 到 {times[-1]}")
+                # 回退：取最近的小时
+                try:
+                    closest = min(times, key=lambda t: abs(
+                        (int(t.split("T")[1].split(":")[0]) if "T" in t else 0) -
+                        (int(time.split(":")[0]))
+                    ))
+                    idx = times.index(closest)
+                    pressure = pressures[idx]
+                    logger.info(f"使用最近时间 {closest} 的气压: {pressure} hPa")
+                    return pressure
+                except Exception:
+                    return None
+        else:
+            logger.warning("API响应中没有hourly数据")
+            return None
+
+    except requests.exceptions.HTTPError:
+        return None
+    except Exception as e:
+        logger.error(f"获取气压时发生未知错误: {type(e).__name__}: {e}")
+        return None
+
 
 def get_pressure_at_location(lat, lon, altitude, date, time, max_retries=3, timeout=30):
     """
-    获取指定位置、时间、高度的气压
+    获取指定位置、时间、高度的气压（公共接口，保持向后兼容）。
 
     Args:
         lat: 纬度
@@ -13,8 +160,8 @@ def get_pressure_at_location(lat, lon, altitude, date, time, max_retries=3, time
         altitude: 海拔高度 (米)
         date: 日期 (格式: YYYY-MM-DD 或 YYYY/MM/DD)
         time: 时间 (格式: HH:MM 或 HH:MM:SS)
-        max_retries: 最大重试次数
-        timeout: 请求超时时间(秒)
+        max_retries: 最大重试次数（已废弃，由内部指数退避处理）
+        timeout: 请求超时时间(秒)（已废弃，由内部控制）
 
     Returns:
         float: 气压值 (hPa)，获取失败返回 None
@@ -22,130 +169,42 @@ def get_pressure_at_location(lat, lon, altitude, date, time, max_retries=3, time
 
     # 标准化日期格式为 YYYY-MM-DD
     def normalize_date(d):
-        """将各种日期格式标准化为 YYYY-MM-DD"""
         if not d:
             from datetime import datetime
             return datetime.now().strftime("%Y-%m-%d")
-
-        # 处理斜杠分隔符
         if "/" in d:
             d = d.replace("/", "-")
-
         parts = d.split("-")
         if len(parts) == 3:
-            year = parts[0]
-            month = parts[1].zfill(2)  # 确保月份是两位数
-            day = parts[2].zfill(2)    # 确保日期是两位数
-            return f"{year}-{month}-{day}"
-        else:
-            # 如果格式不正确，返回今天的日期
-            from datetime import datetime
-            return datetime.now().strftime("%Y-%m-%d")
+            return f"{parts[0]}-{parts[1].zfill(2)}-{parts[2].zfill(2)}"
+        from datetime import datetime
+        return datetime.now().strftime("%Y-%m-%d")
 
     date = normalize_date(date)
 
     # 标准化时间格式为 HH:MM
     def normalize_time(t):
-        """将各种时间格式标准化为 HH:MM"""
         if not t or ":" not in t:
-            return "12:00"  # 默认中午12点
-
+            return "12:00"
         parts = t.split(":")
         if len(parts) >= 2:
-            hour = parts[0].zfill(2)  # 确保小时是两位数
-            minute = parts[1].zfill(2)  # 确保分钟是两位数
-            return f"{hour}:{minute}"
-        elif len(parts) == 1:
-            hour = parts[0].zfill(2)
-            return f"{hour}:00"
-        else:
-            return "12:00"
+            return f"{parts[0].zfill(2)}:{parts[1].zfill(2)}"
+        return "12:00"
 
     time = normalize_time(time)
 
-    url = "https://archive-api.open-meteo.com/v1/archive"
-
-    params = {
-        "latitude": lat,
-        "longitude": lon,
-        "start_date": date,  # 格式: YYYY-MM-DD
-        "end_date": date,
-        "hourly": ["pressure_msl", "surface_pressure"],
-        "timezone": "auto"
-    }
-
-    # 创建带有重试机制的会话
-    session = requests.Session()
-    retry_strategy = Retry(
-        total=max_retries,
-        status_forcelist=[429, 500, 502, 503, 504],
-        backoff_factor=1
-    )
-    adapter = HTTPAdapter(max_retries=retry_strategy)
-    session.mount("http://", adapter)
-    session.mount("https://", adapter)
-
     try:
-        print(f"正在获取位置 ({lat:.6f}, {lon:.6f}) 在 {date} {time} 的气压数据...")
-        response = session.get(url, params=params, timeout=timeout)
-
-        # 检查响应状态
-        response.raise_for_status()
-
-        data = response.json()
-
-        # 检查API错误
-        if "error" in data:
-            print(f"API错误: {data['error']}")
-            return None
-
-        # 解析气压数据
-        if data and "hourly" in data:
-            times = data["hourly"]["time"]
-            pressures = data["hourly"]["surface_pressure"]  # 地表气压
-
-            if not times or not pressures:
-                print("未找到气压数据")
-                return None
-
-            # 根据时间找到对应气压
-            target_time = f"{date}T{time}"
-            if target_time in times:
-                idx = times.index(target_time)
-                pressure = pressures[idx]
-                print(f"成功获取气压: {pressure} hPa")
-                return pressure
-            else:
-                print(f"在数据中未找到时间: {target_time}")
-                print(f"可用时间范围: {times[0]} 到 {times[-1]}")
-                return None
-        else:
-            print("API响应中没有hourly数据")
-            return None
-
-    except requests.exceptions.ConnectionError as e:
-        print(f"网络连接错误: {e}")
-        print("请检查网络连接或稍后重试")
-        return None
-    except requests.exceptions.Timeout as e:
-        print(f"请求超时: {e}")
-        print(f"已重试 {max_retries} 次，请检查网络连接")
-        return None
-    except requests.exceptions.HTTPError as e:
-        print(f"HTTP错误: {e}")
-        return None
-    except ValueError as e:
-        print(f"数据解析错误: {e}")
-        return None
+        return _get_pressure_cached(
+            float(lat), float(lon), float(altitude), date, time
+        )
     except Exception as e:
-        print(f"未知错误: {e}")
+        logger.error(f"get_pressure_at_location 失败: {type(e).__name__}: {e}")
         return None
-    finally:
-        session.close()
+
 
 def batch_get_pressure(data_list):
     """
-    批量获取多个位置的气压数据
+    批量获取多个位置的气压数据。
 
     Args:
         data_list: 包含 (lat, lon, altitude, date, time) 元组的列表
@@ -154,37 +213,36 @@ def batch_get_pressure(data_list):
         list: 气压值列表
     """
     results = []
-    for i, (lat, lon, alt, date, time) in enumerate(data_list):
+    for i, (lat, lon, alt, date, time_val) in enumerate(data_list):
         print(f"\n处理第 {i+1} 个位置...")
-        pressure = get_pressure_at_location(lat, lon, alt, date, time)
+        pressure = get_pressure_at_location(lat, lon, alt, date, time_val)
         results.append(pressure)
         if pressure is not None:
             print(f"位置 {i+1}: {pressure} hPa")
         else:
             print(f"位置 {i+1}: 获取失败")
 
-        # 添加短暂延迟，避免请求过于频繁
+        # 短暂延迟避免请求过于频繁
         if i < len(data_list) - 1:
-            time.sleep(0.5)
+            time.sleep(0.3)
 
     return results
 
+
 # 使用示例
 if __name__ == "__main__":
     print("=== 气压数据获取工具 ===\n")
 
-    # 单个位置示例
     print("1. 单个位置查询:")
     pressure = get_pressure_at_location(
-        lat=40.3491370,        # 纽约纬度
-        lon=115.7855289,       # 纽约经度 (西经)
-        altitude=435.789,         # 海拔10米
+        lat=40.3491370,
+        lon=115.7855289,
+        altitude=435.789,
         date="2016-02-12",
-        time="08:00"        # HH:MM格式
+        time="08:00"
     )
 
     if pressure is not None:
         print(f"纽约当前气压: {pressure} hPa")
     else:
         print("获取纽约气压数据失败")
-