feat: 将模拟平台从西班牙 Demo 数据迁移至中国多省份场景

提交正文：
  - simulator.py: 新增 SyntheticWeatherDataProvider，基于河南、黑龙江、湖北、
    新疆、四川五省气候模板生成合成日气象数据
  - simulator.py: 引入国内农事日历 CROP_CALENDAR_CN 与省份作物配置 PROVINCE_CROPS
  - simulator.py: 移除对 GridWeatherDataProvider / AgroManagementDataProvider /
    fetch_sitedata 的西班牙 Demo 数据依赖
  - app.py: 侧边栏支持省份选择，年份范围扩展为 2019-2023
  - app.py: 全面移除自定义 CSS，改用 Streamlit 原生组件（st.metric / st.info /
    st.success / st.divider 等）简化界面
  - app.py: 图表回归 Plotly 原生 add_vline，移除 hex_to_rgba / add_milestone_line
    辅助函数

app.py

@@ -4,39 +4,15 @@
 
 import streamlit as st
 import plotly.graph_objects as go
-from plotly.subplots import make_subplots
 
-from simulator import run_wofost, run_multi_crop, list_available_crops, CROP_META
+from simulator import (
+    run_wofost,
+    run_multi_crop,
+    list_available_provinces,
+    list_available_crops,
+    CROP_META,
+)
 
-
-def hex_to_rgba(hex_color: str, alpha: float = 0.1) -> str:
-    hex_color = hex_color.lstrip("#")
-    r = int(hex_color[0:2], 16)
-    g = int(hex_color[2:4], 16)
-    b = int(hex_color[4:6], 16)
-    return f"rgba({r}, {g}, {b}, {alpha})"
-
-
-def add_milestone_line(fig, x, color: str, text: str, xref: str = "x"):
-    """用 shape + annotation 手动添加竖线，绕过 Plotly add_vline 对 datetime.date 的 bug。"""
-    fig.add_shape(
-        type="line",
-        x0=x, x1=x, y0=0, y1=1,
-        xref=xref, yref="paper",
-        line=dict(color=color, width=1.5, dash="dot"),
-    )
-    fig.add_annotation(
-        x=x, y=1.02,
-        xref=xref, yref="paper",
-        text=text,
-        showarrow=False,
-        font=dict(color=color, size=10),
-        bgcolor="rgba(255,255,255,0.8)",
-        borderpad=2,
-    )
-
-
-# ─── Page Config ────────────────────────────────────────────────────────────
 st.set_page_config(
     page_title="麦麦智农",
     page_icon="🌾",
@@ -44,279 +20,108 @@ st.set_page_config(
     initial_sidebar_state="expanded",
 )
 
-# ─── Custom CSS ──────────────────────────────────────────────────────────────
-st.markdown("""
-<style>
-:root {
-    --soil: #7a5c44;
-    --leaf: #4a7c59;
-    --leaf-light: #6b9e75;
-    --wheat: #d4a574;
-    --cream: #faf8f3;
-    --paper: #ffffff;
-    --ink: #2c2c2c;
-    --ink-muted: #5a5a5a;
-    --border: #e5e0d5;
-    --shadow: rgba(0,0,0,0.04);
-    --warn: #c98c5b;
-}
 
-html, body, [class*="css"] {
-    font-family: "PingFang SC", "Microsoft YaHei", "Noto Sans SC", sans-serif;
-    color: var(--ink);
-}
-
-.stApp {
-    background: var(--cream);
-}
-
-/* Sidebar */
-[data-testid="stSidebar"] {
-    background: #f5f2eb;
-    border-right: 1px solid var(--border);
-}
-[data-testid="stSidebar"] .stSlider label,
-[data-testid="stSidebar"] .stNumberInput label,
-[data-testid="stSidebar"] .stSelectbox label {
-    color: var(--soil) !important;
-    font-size: 0.85rem;
-    font-weight: 500;
-}
-
-/* Metric cards */
-.metric-card {
-    background: var(--paper);
-    border: 1px solid var(--border);
-    border-radius: 14px;
-    padding: 18px 16px;
-    text-align: center;
-    box-shadow: 0 2px 10px var(--shadow);
-}
-.metric-value {
-    font-size: 1.8rem;
-    font-weight: 700;
-    color: var(--leaf);
-    line-height: 1.1;
-}
-.metric-unit {
-    font-size: 0.75rem;
-    color: var(--ink-muted);
-    margin-top: 4px;
-}
-.metric-label {
-    font-size: 0.9rem;
-    color: var(--ink);
-    margin-top: 8px;
-    font-weight: 500;
-}
-
-/* Section headers */
-.section-header {
-    font-size: 0.95rem;
-    font-weight: 600;
-    color: var(--soil);
-    padding-bottom: 8px;
-    margin-bottom: 14px;
-    margin-top: 22px;
-    border-bottom: 1px solid var(--border);
-}
-
-/* Hero */
-.hero-title {
-    font-size: 1.7rem;
-    font-weight: 700;
-    color: var(--soil);
-    line-height: 1.2;
-}
-.hero-sub {
-    font-size: 0.85rem;
-    color: var(--ink-muted);
-    margin-top: 4px;
-}
-
-/* Alert boxes */
-.alert-good {
-    background: #f4faf5;
-    border-left: 3px solid var(--leaf-light);
-    border-radius: 0 8px 8px 0;
-    padding: 12px 14px;
-    margin: 8px 0;
-    font-size: 0.9rem;
-    color: var(--ink);
-}
-.alert-warn {
-    background: #fdf9f3;
-    border-left: 3px solid var(--wheat);
-    border-radius: 0 8px 8px 0;
-    padding: 12px 14px;
-    margin: 8px 0;
-    font-size: 0.9rem;
-    color: var(--ink);
-}
-
-/* Sidebar title */
-.sidebar-title {
-    font-size: 1.15rem;
-    font-weight: 700;
-    color: var(--soil);
-    margin-bottom: 2px;
-}
-.sidebar-sub {
-    font-size: 0.75rem;
-    color: var(--ink-muted);
-    margin-bottom: 12px;
-}
-
-/* Streamlit overrides */
-.stButton > button {
-    border-radius: 10px !important;
-    background: var(--leaf) !important;
-    border: none !important;
-    color: #fff !important;
-}
-.stButton > button:hover {
-    background: var(--leaf-light) !important;
-}
-
-/* Info panel */
-.info-panel {
-    background: var(--paper);
-    border: 1px solid var(--border);
-    border-radius: 12px;
-    padding: 14px 16px;
-    font-size: 0.85rem;
-    color: var(--ink-muted);
-    line-height: 1.6;
-}
-</style>
-""", unsafe_allow_html=True)
-
-
-# ─── Sidebar Inputs ──────────────────────────────────────────────────────────
+# ─── Sidebar ────────────────────────────────────────────────────────────────
 with st.sidebar:
-    st.markdown('<div class="sidebar-title">🌾 麦麦智农</div>', unsafe_allow_html=True)
-    st.markdown('<div class="sidebar-sub">基于 PCSE/WOFOST 的真实作物模拟</div>', unsafe_allow_html=True)
-    st.markdown("<hr style='border:none;border-top:1px solid var(--border);margin:12px 0;'>", unsafe_allow_html=True)
+    st.header("🌾 麦麦智农")
+    st.caption("基于 PCSE/WOFOST 的真实作物模拟")
+    st.divider()
 
-    st.markdown('<div class="section-header" style="margin-top:0">🗺 模拟地点</div>', unsafe_allow_html=True)
-    st.markdown("""
-    <div class="info-panel" style="margin-bottom:14px;">
-        <b>西班牙 · 埃斯特雷马杜拉</b><br>
-        Grid 31031 (37.64°N, 6.09°W)
-    </div>
-    """, unsafe_allow_html=True)
+    province = st.selectbox("省份", list_available_provinces())
+    year = st.selectbox("年份", [2019, 2020, 2021, 2022, 2023], index=3)
 
-    st.markdown('<div class="section-header">📅 模拟参数</div>', unsafe_allow_html=True)
-    year = st.selectbox("年份", [2000], index=0, help="Demo 数据库当前仅提供 2000 年数据")
-
-    crops = list_available_crops(grid_no=31031, year=year)
+    crops = list_available_crops(province)
     crop_options = {c["crop_no"]: f"{c['emoji']} {c['name']}" for c in crops}
-    selected_crop_no = st.selectbox("作物", list(crop_options.keys()), format_func=lambda k: crop_options[k])
+    selected_crop_no = st.selectbox(
+        "作物", list(crop_options.keys()), format_func=lambda k: crop_options[k]
+    )
 
-    mode = st.radio("生产模式", ["pp", "wlp"], format_func=lambda m: {"pp": "潜在生产 (PP)", "wlp": "水分限制生产 (WLP)"}[m])
+    mode = st.radio(
+        "生产模式",
+        ["pp", "wlp"],
+        format_func=lambda m: {"pp": "潜在生产 (PP)", "wlp": "水分限制生产 (WLP)"}[m],
+    )
 
-    st.markdown('<div class="section-header">🌱 种植规模</div>', unsafe_allow_html=True)
-    area = st.number_input("种植面积 (公顷)", min_value=0.1, max_value=10000.0, value=100.0, step=10.0)
+    area = st.number_input(
+        "种植面积 (公顷)", min_value=0.1, max_value=10000.0, value=100.0, step=10.0
+    )
 
 
-# ─── Run Simulation ──────────────────────────────────────────────────────────
+# ─── Run Simulation ─────────────────────────────────────────────────────────
 @st.cache_data(show_spinner=False)
-def cached_run(crop_no, year, mode):
-    return run_wofost(crop_no=crop_no, year=year, mode=mode)
+def cached_run(province, crop_no, year, mode):
+    return run_wofost(province=province, crop_no=crop_no, year=year, mode=mode)
 
 
 @st.cache_data(show_spinner=False)
-def cached_multi_crop(year, mode):
-    return run_multi_crop(year=year, mode=mode)
+def cached_multi_crop(province, year, mode):
+    return run_multi_crop(province=province, year=year, mode=mode)
 
 
 with st.spinner("正在运行 WOFOST 作物模拟，请稍候..."):
-    result = cached_run(selected_crop_no, year, mode)
-    multi_crop_result = cached_multi_crop(year, mode)
+    result = cached_run(province, selected_crop_no, year, mode)
+    multi_crop_result = cached_multi_crop(province, year, mode)
 
 meta = result["meta"]
 summary = result["summary"]
 df = result["df"]
 crop_info = CROP_META.get(selected_crop_no, {"name": "未知作物", "emoji": "🌱", "color": "#888"})
 
-# 单位换算
 twso = summary["twso"] if summary["twso"] is not None else 0.0
 tagp = summary["tagp"] if summary["tagp"] is not None else 0.0
 max_lai = summary["max_lai"] if summary["max_lai"] is not None else 0.0
 total_twso_tons = twso * area / 1000.0
 
-# ─── Main Layout ─────────────────────────────────────────────────────────────
-st.markdown(f"""
-<div style="display:flex; align-items:baseline; gap:12px; margin-bottom:4px;">
-  <div class="hero-title">麦麦智农 · {crop_info['emoji']} {crop_info['name']} 生长模拟</div>
-</div>
-<div class="hero-sub">
-    {meta['year']} 年 · {meta['mode_label']} · 基于 WOFOST 7.2 真实作物模型
-</div>
-""", unsafe_allow_html=True)
+# ─── Header ─────────────────────────────────────────────────────────────────
+st.title(f"{crop_info['emoji']} {crop_info['name']} 生长模拟")
+st.caption(f"{province} · {year} 年 · {meta['mode_label']} · 基于 WOFOST 7.2 真实作物模型")
 
-st.markdown("<br>", unsafe_allow_html=True)
-
-# KPI row
+# ─── KPIs ───────────────────────────────────────────────────────────────────
 k1, k2, k3, k4 = st.columns(4)
-with k1:
-    st.markdown(f"""
-    <div class="metric-card">
-        <div class="metric-value">{twso:,.0f}</div>
-        <div class="metric-unit">kg / 公顷</div>
-        <div class="metric-label">🌾 经济产量 (TWSO)</div>
-    </div>""", unsafe_allow_html=True)
-with k2:
-    st.markdown(f"""
-    <div class="metric-card">
-        <div class="metric-value">{tagp:,.0f}</div>
-        <div class="metric-unit">kg / 公顷</div>
-        <div class="metric-label">🌿 总生物量 (TAGP)</div>
-    </div>""", unsafe_allow_html=True)
-with k3:
-    st.markdown(f"""
-    <div class="metric-card">
-        <div class="metric-value">{max_lai:.2f}</div>
-        <div class="metric-unit">最大叶面积指数</div>
-        <div class="metric-label">☘️ LAI max</div>
-    </div>""", unsafe_allow_html=True)
-with k4:
-    st.markdown(f"""
-    <div class="metric-card">
-        <div class="metric-value">{total_twso_tons:,.1f}</div>
-        <div class="metric-unit">吨 / 总产量</div>
-        <div class="metric-label">📦 {area:.0f} 公顷总产</div>
-    </div>""", unsafe_allow_html=True)
+k1.metric("经济产量 (TWSO)", f"{twso:,.0f} kg/ha")
+k2.metric("总生物量 (TAGP)", f"{tagp:,.0f} kg/ha")
+k3.metric("最大叶面积指数 (LAI)", f"{max_lai:.2f}")
+k4.metric(f"{area:.0f} 公顷总产量", f"{total_twso_tons:,.1f} 吨")
 
-st.markdown("<br>", unsafe_allow_html=True)
+st.divider()
 
-# ─── Charts Row 1 ────────────────────────────────────────────────────────────
+# ─── Charts Row 1 ───────────────────────────────────────────────────────────
 chart_left, chart_right = st.columns(2)
 
 with chart_left:
-    st.markdown('<div class="section-header">📈 叶面积指数 (LAI) 动态</div>', unsafe_allow_html=True)
+    st.subheader("📈 叶面积指数 (LAI) 动态")
     if not df.empty and "LAI" in df.columns:
         fig_lai = go.Figure()
-        fig_lai.add_trace(go.Scatter(
-            x=df.index, y=df["LAI"],
-            mode="lines",
-            line=dict(color=crop_info["color"], width=2.5),
-            fill="tozeroy",
-            fillcolor=hex_to_rgba(crop_info["color"], 0.1),
-            name="LAI",
-        ))
-        # 添加生育期标记
+        fig_lai.add_trace(
+            go.Scatter(
+                x=df.index,
+                y=df["LAI"],
+                mode="lines",
+                line=dict(color=crop_info["color"], width=2.5),
+                fill="tozeroy",
+                name="LAI",
+            )
+        )
         ms = summary.get("milestones", {})
         if "flowering" in ms:
-            add_milestone_line(fig_lai, x=ms["flowering"], color="#d4a574", text="开花")
+            fig_lai.add_vline(
+                x=ms["flowering"],
+                line=dict(color="#d4a574", width=1.5, dash="dot"),
+                annotation_text="开花",
+                annotation_position="top left",
+            )
         if "maturity" in ms:
-            add_milestone_line(fig_lai, x=ms["maturity"], color="#7c5e42", text="成熟")
+            fig_lai.add_vline(
+                x=ms["maturity"],
+                line=dict(color="#7c5e42", width=1.5, dash="dash"),
+                annotation_text="成熟",
+                annotation_position="top right",
+            )
         fig_lai.update_layout(
-            xaxis=dict(title="日期", color="#5a5a5a", gridcolor="rgba(0,0,0,0.06)"),
-            yaxis=dict(title="LAI", color="#5a5a5a", gridcolor="rgba(0,0,0,0.06)"),
-            paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(0,0,0,0)",
-            font=dict(color="#2c2c2c", size=10),
-            margin=dict(t=20, b=36, l=50, r=20), height=280,
+            xaxis_title="日期",
+            yaxis_title="LAI",
+            margin=dict(t=20, b=36, l=50, r=20),
+            height=300,
             showlegend=False,
         )
         st.plotly_chart(fig_lai, use_container_width=True)
@@ -324,60 +129,62 @@ with chart_left:
         st.info("暂无 LAI 数据")
 
 with chart_right:
-    st.markdown('<div class="section-header">📊 生物量与产量积累</div>', unsafe_allow_html=True)
+    st.subheader("📊 生物量与产量积累")
     if not df.empty and "TAGP" in df.columns:
         fig_bio = go.Figure()
-        fig_bio.add_trace(go.Scatter(
-            x=df.index, y=df["TAGP"],
-            mode="lines",
-            line=dict(color="#7a9e7e", width=2.5),
-            name="TAGP",
-        ))
+        fig_bio.add_trace(
+            go.Scatter(
+                x=df.index, y=df["TAGP"], mode="lines", name="TAGP", line=dict(color="#7a9e7e", width=2.5)
+            )
+        )
         if "TWSO" in df.columns:
-            fig_bio.add_trace(go.Scatter(
-                x=df.index, y=df["TWSO"],
-                mode="lines",
-                line=dict(color=crop_info["color"], width=2.5),
-                name="TWSO",
-            ))
+            fig_bio.add_trace(
+                go.Scatter(
+                    x=df.index,
+                    y=df["TWSO"],
+                    mode="lines",
+                    name="TWSO",
+                    line=dict(color=crop_info["color"], width=2.5),
+                )
+            )
         ms = summary.get("milestones", {})
         if "flowering" in ms:
-            add_milestone_line(fig_bio, x=ms["flowering"], color="#d4a574", text="开花")
+            fig_bio.add_vline(x=ms["flowering"], line=dict(color="#d4a574", width=1.5, dash="dot"))
         if "maturity" in ms:
-            add_milestone_line(fig_bio, x=ms["maturity"], color="#7c5e42", text="成熟")
+            fig_bio.add_vline(x=ms["maturity"], line=dict(color="#7c5e42", width=1.5, dash="dash"))
         fig_bio.update_layout(
-            xaxis=dict(title="日期", color="#5a5a5a", gridcolor="rgba(0,0,0,0.06)"),
-            yaxis=dict(title="干物质 (kg/ha)", color="#5a5a5a", gridcolor="rgba(0,0,0,0.06)"),
-            paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(0,0,0,0)",
-            font=dict(color="#2c2c2c", size=10),
-            margin=dict(t=20, b=36, l=50, r=20), height=280,
-            legend=dict(orientation="h", y=-0.18, font=dict(size=10)),
+            xaxis_title="日期",
+            yaxis_title="干物质 (kg/ha)",
+            margin=dict(t=20, b=36, l=50, r=20),
+            height=300,
+            legend=dict(orientation="h", y=-0.18),
         )
         st.plotly_chart(fig_bio, use_container_width=True)
     else:
         st.info("暂无生物量数据")
 
-# ─── Charts Row 2 ────────────────────────────────────────────────────────────
+# ─── Charts Row 2 ───────────────────────────────────────────────────────────
 chart2_left, chart2_right = st.columns(2)
 
 with chart2_left:
-    st.markdown('<div class="section-header">💧 土壤水分动态</div>', unsafe_allow_html=True)
+    st.subheader("💧 土壤水分动态")
     if not df.empty and "SM" in df.columns:
         fig_sm = go.Figure()
-        fig_sm.add_trace(go.Scatter(
-            x=df.index, y=df["SM"],
-            mode="lines",
-            line=dict(color="#5a8f9e", width=2.5),
-            fill="tozeroy",
-            fillcolor="rgba(90, 143, 158, 0.1)",
-            name="SM",
-        ))
+        fig_sm.add_trace(
+            go.Scatter(
+                x=df.index,
+                y=df["SM"],
+                mode="lines",
+                line=dict(color="#5a8f9e", width=2.5),
+                fill="tozeroy",
+                name="SM",
+            )
+        )
         fig_sm.update_layout(
-            xaxis=dict(title="日期", color="#5a5a5a", gridcolor="rgba(0,0,0,0.06)"),
-            yaxis=dict(title="土壤含水量 (cm³/cm³)", color="#5a5a5a", gridcolor="rgba(0,0,0,0.06)"),
-            paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(0,0,0,0)",
-            font=dict(color="#2c2c2c", size=10),
-            margin=dict(t=20, b=36, l=50, r=20), height=260,
+            xaxis_title="日期",
+            yaxis_title="土壤含水量 (cm³/cm³)",
+            margin=dict(t=20, b=36, l=50, r=20),
+            height=280,
             showlegend=False,
         )
         st.plotly_chart(fig_sm, use_container_width=True)
@@ -385,84 +192,87 @@ with chart2_left:
         st.info("暂无土壤水分数据")
 
 with chart2_right:
-    st.markdown('<div class="section-header">🏅 同一年份作物产量对比</div>', unsafe_allow_html=True)
+    st.subheader("🏅 同一年份作物产量对比")
     if multi_crop_result:
-        names = [f"{CROP_META.get(r['crop_no'], {}).get('emoji', '🌱')} {r['name']}" for r in multi_crop_result]
+        names = [
+            f"{CROP_META.get(r['crop_no'], {}).get('emoji', '🌱')} {r['name']}"
+            for r in multi_crop_result
+        ]
         twsos = [r["twso"] if r["twso"] is not None else 0.0 for r in multi_crop_result]
         colors = [CROP_META.get(r["crop_no"], {}).get("color", "#888") for r in multi_crop_result]
         fig_bar = go.Figure()
-        fig_bar.add_trace(go.Bar(
-            x=names, y=twsos,
-            marker=dict(color=colors, opacity=0.85,
-                        line=dict(color="rgba(0,0,0,0.08)", width=1)),
-            text=[f"{v:,.0f}" for v in twsos],
-            textposition="outside",
-            textfont=dict(color="#5a5a5a", size=10),
-        ))
+        fig_bar.add_trace(
+            go.Bar(
+                x=names,
+                y=twsos,
+                marker=dict(color=colors, opacity=0.85),
+                text=[f"{v:,.0f}" for v in twsos],
+                textposition="outside",
+            )
+        )
         fig_bar.update_layout(
-            xaxis=dict(color="#5a5a5a", gridcolor="rgba(0,0,0,0.04)"),
-            yaxis=dict(title="经济产量 (kg/ha)", color="#5a5a5a", gridcolor="rgba(0,0,0,0.06)"),
-            paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(0,0,0,0)",
-            font=dict(color="#2c2c2c", size=11),
-            margin=dict(t=20, b=30, l=60, r=20), height=260,
+            yaxis_title="经济产量 (kg/ha)",
+            margin=dict(t=20, b=30, l=60, r=20),
+            height=280,
             showlegend=False,
         )
         st.plotly_chart(fig_bar, use_container_width=True)
     else:
         st.info("暂无对比数据")
 
-# ─── Advisory Panel ───────────────────────────────────────────────────────────
-st.markdown('<div class="section-header">💡 农艺建议与生育期</div>', unsafe_allow_html=True)
+# ─── Advisory Panel ─────────────────────────────────────────────────────────
+st.divider()
+st.subheader("💡 农艺建议与生育期")
+
 adv1, adv2 = st.columns(2)
 
 with adv1:
-    advisories = []
     if twso > 6000:
-        advisories.append(("good", f"{crop_info['name']} 模拟产量达到 {twso:,.0f} kg/ha，属于高产水平，气候与土壤条件匹配良好。"))
+        st.success(
+            f"{crop_info['name']} 模拟产量达到 {twso:,.0f} kg/ha，属于高产水平，气候与土壤条件匹配良好。"
+        )
     elif twso > 3000:
-        advisories.append(("good", f"{crop_info['name']} 模拟产量为 {twso:,.0f} kg/ha，处于中等水平，可通过优化水肥管理进一步提升。"))
+        st.info(
+            f"{crop_info['name']} 模拟产量为 {twso:,.0f} kg/ha，处于中等水平，可通过优化水肥管理进一步提升。"
+        )
     else:
-        advisories.append(("warn", f"{crop_info['name']} 模拟产量仅 {twso:,.0f} kg/ha，建议检查品种适宜性或水分胁迫情况。"))
+        st.warning(
+            f"{crop_info['name']} 模拟产量仅 {twso:,.0f} kg/ha，建议检查品种适宜性或水分胁迫情况。"
+        )
 
     if mode == "wlp":
         if not df.empty and "SM" in df.columns:
             sm_min = df["SM"].min()
             if sm_min < 0.15:
-                advisories.append(("warn", f"模拟期间土壤水分最低降至 {sm_min:.3f}，出现明显水分胁迫，建议评估灌溉方案。"))
+                st.warning(
+                    f"模拟期间土壤水分最低降至 {sm_min:.3f}，出现明显水分胁迫，建议评估灌溉方案。"
+                )
             else:
-                advisories.append(("good", "模拟期间土壤水分状况总体良好，未出现极端干旱胁迫。"))
+                st.success("模拟期间土壤水分状况总体良好，未出现极端干旱胁迫。")
     else:
-        advisories.append(("good", "当前为潜在生产模式，结果反映理想水肥条件下的产量上限。"))
+        st.info("当前为潜在生产模式，结果反映理想水肥条件下的产量上限。")
 
     if max_lai > 5:
-        advisories.append(("good", f"最大 LAI 达到 {max_lai:.2f}，冠层覆盖充分，光能截获效率高。"))
+        st.success(f"最大 LAI 达到 {max_lai:.2f}，冠层覆盖充分，光能截获效率高。")
     elif max_lai < 2:
-        advisories.append(("warn", f"最大 LAI 仅 {max_lai:.2f}，冠层发育不足，可能存在播期或品种问题。"))
-
-    for typ, msg in advisories:
-        css_class = "alert-good" if typ == "good" else "alert-warn"
-        st.markdown(f'<div class="{css_class}">{msg}</div>', unsafe_allow_html=True)
+        st.warning(f"最大 LAI 仅 {max_lai:.2f}，冠层发育不足，可能存在播期或品种问题。")
 
 with adv2:
     ms = summary.get("milestones", {})
     if ms:
-        st.markdown(f"""
-        <div class="info-panel">
-            <b>生育期里程碑</b><br>
-            播种/出苗：{ms.get('start', '—')}<br>
-            开花期：{ms.get('flowering', '—')}<br>
-            成熟期：{ms.get('maturity', '—')}<br>
-            收获/结束：{ms.get('end', '—')}<br>
-            <span style="color:#999;">生育期天数：{summary.get('duration', '—')} 天</span>
-        </div>
-        """, unsafe_allow_html=True)
-    else:
-        st.markdown('<div class="info-panel">暂无生育期数据</div>', unsafe_allow_html=True)
+        st.info(
+            f"""
+**生育期里程碑**
 
-# ─── Footer ───────────────────────────────────────────────────────────────────
-st.markdown("<br>", unsafe_allow_html=True)
-st.markdown("""
-<div style="text-align:center; font-size:0.78rem; color:#aaa; padding:14px; border-top:1px solid #e5e0d5;">
-  麦麦智农 · 基于 PCSE/WOFOST 真实作物生长模型 · 结果仅供参考
-</div>
-""", unsafe_allow_html=True)
+- 播种/出苗：{ms.get('start', '—')}
+- 开花期：{ms.get('flowering', '—')}
+- 成熟期：{ms.get('maturity', '—')}
+- 收获/结束：{ms.get('end', '—')}
+- 生育期天数：{summary.get('duration', '—')} 天
+            """
+        )
+    else:
+        st.info("暂无生育期数据")
+
+st.divider()
+st.caption("麦麦智农 · 基于 PCSE/WOFOST 真实作物生长模型 · 结果仅供参考")

simulator.py

@@ -1,37 +1,27 @@
 """
-麦麦智农 - PCSE 模拟引擎封装
+麦麦智农 - PCSE 模拟引擎封装（国内场景版）
 基于 WOFOST 7.2 潜在生产（PP）与水分限制生产（WLP）模型
 """
 
+import datetime
+import math
 import sqlite3
 from collections import namedtuple
 from typing import Literal
 
+import numpy as np
 import pandas as pd
 
-import pcse
 from pcse.models import Wofost72_PP, Wofost72_WLP_CWB
 from pcse.settings import settings
-from pcse.base import ParameterProvider
-from pcse.tests.db_input import (
-    GridWeatherDataProvider,
-    fetch_soildata,
-    fetch_sitedata,
-    fetch_cropdata,
-    AgroManagementDataProvider,
-)
+from pcse.base import ParameterProvider, WeatherDataContainer, WeatherDataProvider
+from pcse.tests.db_input import fetch_soildata, fetch_cropdata
+from pcse.util import reference_ET
 
 
-def namedtuple_factory(cursor, row):
-    fields = [column[0] for column in cursor.description]
-    cls = namedtuple("Row", fields)
-    return cls._make(row)
-
-
-# Demo 数据库配置
+# ─── 常量配置 ────────────────────────────────────────────────────────────────
 DB_PATH = settings.PCSE_USER_HOME + "/pcse.db"
 
-# 作物映射（crop_no -> 中文名/元数据）
 CROP_META = {
     1: {"name": "冬小麦", "emoji": "🌾", "color": "#c69c5d", "en": "WINTER WHEAT"},
     2: {"name": "玉米", "emoji": "🌽", "color": "#e8a93f", "en": "GRAIN MAIZE"},
@@ -46,6 +36,129 @@ MODE_LABELS = {
     "wlp": "水分限制生产",
 }
 
+# 省份支持作物（黑龙江太冷，去掉越冬作物）
+PROVINCE_CROPS = {
+    "河南": [1, 2, 3, 7, 10, 11],
+    "黑龙江": [2, 3, 7, 11],
+    "湖北": [1, 2, 3, 7, 10, 11],
+    "新疆": [1, 2, 3, 7, 10, 11],
+    "四川": [1, 2, 3, 7, 10, 11],
+}
+
+# 国内农事日历（播种月日 -> 收获月日）
+CROP_CALENDAR_CN = {
+    "冬小麦": {
+        "河南": {"start": (10, 15), "end": (5, 25)},
+        "湖北": {"start": (10, 25), "end": (5, 15)},
+        "新疆": {"start": (9, 20), "end": (6, 25)},
+        "四川": {"start": (10, 20), "end": (5, 10)},
+    },
+    "玉米": {
+        "河南": {"start": (6, 5), "end": (9, 25)},
+        "黑龙江": {"start": (5, 1), "end": (9, 20)},
+        "湖北": {"start": (3, 25), "end": (7, 20)},
+        "新疆": {"start": (4, 15), "end": (9, 10)},
+        "四川": {"start": (3, 20), "end": (7, 15)},
+    },
+    "春大麦": {
+        "河南": {"start": (3, 1), "end": (5, 25)},
+        "黑龙江": {"start": (4, 10), "end": (7, 15)},
+        "湖北": {"start": (2, 15), "end": (5, 20)},
+        "新疆": {"start": (3, 25), "end": (7, 10)},
+        "四川": {"start": (2, 20), "end": (5, 15)},
+    },
+    "马铃薯": {
+        "河南": {"start": (2, 25), "end": (5, 30)},
+        "黑龙江": {"start": (4, 20), "end": (8, 15)},
+        "湖北": {"start": (1, 15), "end": (5, 10)},
+        "新疆": {"start": (4, 1), "end": (7, 25)},
+        "四川": {"start": (1, 20), "end": (5, 5)},
+    },
+    "冬油菜": {
+        "河南": {"start": (9, 15), "end": (5, 15)},
+        "湖北": {"start": (9, 20), "end": (5, 5)},
+        "新疆": {"start": (8, 25), "end": (6, 15)},
+        "四川": {"start": (9, 10), "end": (4, 25)},
+    },
+    "向日葵": {
+        "河南": {"start": (5, 25), "end": (8, 30)},
+        "黑龙江": {"start": (5, 15), "end": (9, 10)},
+        "湖北": {"start": (4, 5), "end": (8, 5)},
+        "新疆": {"start": (4, 20), "end": (8, 20)},
+        "四川": {"start": (3, 25), "end": (7, 25)},
+    },
+}
+
+# 省份气候模板（每月 [TMAX, TMIN, RAIN(mm/day), IRRAD(kJ/m2/day)]）
+PROVINCES = {
+    "河南": {
+        "lat": 34.76, "lon": 113.65, "elev": 110,
+        "rh": 0.65, "wind_base": 2.0,
+        "climate": [
+            [5, -4, 0.5, 9000], [9, -1, 0.6, 12000], [15, 4, 1.0, 16000],
+            [22, 10, 1.5, 19000], [28, 16, 2.0, 22000], [33, 21, 2.5, 24000],
+            [32, 23, 4.0, 21000], [31, 22, 3.5, 19000], [27, 17, 2.0, 16000],
+            [22, 11, 1.2, 13000], [14, 3, 0.8, 9000], [7, -2, 0.4, 8000],
+        ],
+    },
+    "黑龙江": {
+        "lat": 45.80, "lon": 126.53, "elev": 150,
+        "rh": 0.60, "wind_base": 3.0,
+        "climate": [
+            [-13, -24, 0.2, 6000], [-8, -20, 0.2, 9000], [2, -9, 0.4, 14000],
+            [13, 1, 1.0, 18000], [21, 8, 1.8, 22000], [26, 15, 3.0, 24000],
+            [28, 18, 4.5, 22000], [26, 16, 4.0, 19000], [20, 9, 1.5, 15000],
+            [11, 1, 0.8, 11000], [-1, -10, 0.4, 7000], [-11, -21, 0.3, 5000],
+        ],
+    },
+    "湖北": {
+        "lat": 30.59, "lon": 114.31, "elev": 30,
+        "rh": 0.75, "wind_base": 1.8,
+        "climate": [
+            [8, 0, 1.5, 8000], [11, 3, 1.8, 10000], [16, 7, 2.5, 13000],
+            [22, 13, 3.0, 16000], [27, 18, 3.5, 19000], [30, 22, 4.5, 20000],
+            [33, 25, 5.5, 22000], [33, 25, 4.0, 21000], [29, 21, 3.0, 17000],
+            [24, 15, 2.0, 14000], [17, 8, 1.5, 10000], [11, 2, 1.0, 8000],
+        ],
+    },
+    "新疆": {
+        "lat": 44.30, "lon": 87.60, "elev": 450,
+        "rh": 0.45, "wind_base": 2.5,
+        "climate": [
+            [-8, -16, 0.1, 7000], [-4, -12, 0.2, 11000], [6, -2, 0.3, 16000],
+            [18, 7, 0.5, 20000], [25, 13, 0.8, 24000], [30, 18, 1.0, 26000],
+            [32, 20, 1.2, 27000], [30, 18, 0.8, 25000], [24, 12, 0.5, 20000],
+            [15, 4, 0.3, 15000], [3, -4, 0.2, 9000], [-5, -13, 0.1, 6000],
+        ],
+    },
+    "四川": {
+        "lat": 30.67, "lon": 104.07, "elev": 500,
+        "rh": 0.80, "wind_base": 1.2,
+        "climate": [
+            [10, 3, 0.6, 6000], [13, 5, 0.8, 8000], [17, 9, 1.2, 11000],
+            [23, 14, 2.0, 13000], [27, 18, 3.0, 15000], [29, 21, 4.0, 16000],
+            [31, 23, 5.0, 17000], [31, 23, 4.5, 16000], [27, 19, 3.0, 12000],
+            [22, 15, 1.5, 9000], [16, 10, 1.0, 7000], [11, 5, 0.5, 6000],
+        ],
+    },
+}
+
+SITEDATA = {
+    "IFUNRN": 0.0,
+    "SSMAX": 0.0,
+    "NOTINF": 0.0,
+    "SSI": 0.0,
+    "WAV": 100.0,
+    "SMLIM": 0.0,
+}
+
+
+# ─── 数据库辅助 ───────────────────────────────────────────────────────────────
+def namedtuple_factory(cursor, row):
+    fields = [column[0] for column in cursor.description]
+    cls = namedtuple("Row", fields)
+    return cls._make(row)
+
 
 def get_db_conn():
     conn = sqlite3.connect(DB_PATH)
@@ -53,68 +166,172 @@ def get_db_conn():
     return conn
 
 
-def list_available_years(grid_no: int = 31031) -> list[int]:
-    """返回 Demo 数据库中指定 grid 有气象数据的年份列表。"""
-    conn = get_db_conn()
-    c = conn.cursor()
-    rows = c.execute(
-        "SELECT DISTINCT strftime('%Y', day) as year FROM grid_weather WHERE grid_no=? ORDER BY year",
-        (grid_no,),
-    ).fetchall()
-    conn.close()
-    return [int(r.year) for r in rows]
+# ─── 气象数据生成器 ───────────────────────────────────────────────────────────
+class SyntheticWeatherDataProvider(WeatherDataProvider):
+    """基于省份气候模板和年份生成合成日气象数据。"""
+
+    angstA = 0.29
+    angstB = 0.49
+
+    def __init__(self, province: str, start_date: datetime.date, end_date: datetime.date, seed: int):
+        WeatherDataProvider.__init__(self)
+        self.province = PROVINCES[province]
+        self.latitude = self.province["lat"]
+        self.longitude = self.province["lon"]
+        self.elevation = self.province["elev"]
+        self.description = f"Synthetic weather for {province}"
+
+        rng = np.random.default_rng(seed)
+        days = (end_date - start_date).days + 1
+        days_in_month = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
+        if (start_date.year % 4 == 0 and start_date.year % 100 != 0) or (start_date.year % 400 == 0):
+            days_in_month[1] = 29
+
+        for i in range(days):
+            day = start_date + datetime.timedelta(days=i)
+            month = day.month - 1
+            next_month = (month + 1) % 12
+            d = day.day - 1
+            frac = (d + 0.5) / days_in_month[month]
+            w2 = 0.5 - 0.5 * math.cos(math.pi * frac)
+            w1 = 1 - w2
+
+            c = self.province["climate"][month]
+            c_next = self.province["climate"][next_month]
+
+            tmax_base = c[0] * w1 + c_next[0] * w2
+            tmin_base = c[1] * w1 + c_next[1] * w2
+            rain_mean = c[2] * w1 + c_next[2] * w2
+            irrad_base = (c[3] * w1 + c_next[3] * w2) * 1000.0  # kJ -> J
+
+            tmax = tmax_base + rng.normal(0, 1.5)
+            tmin = tmin_base + rng.normal(0, 1.5)
+            tmin = min(tmin, tmax - 1.0)
+            irrad = max(0, irrad_base * (1 + rng.normal(0, 0.1)))
+
+            p_rain = min(0.8, max(0.05, rain_mean / 5.0))
+            if rng.random() < p_rain:
+                scale = rain_mean / (2 * p_rain)
+                rain = rng.gamma(2, scale) if scale > 0 else 0.0
+            else:
+                rain = 0.0
+            rain = max(0.0, rain)
+
+            tmean = (tmax + tmin) / 2.0
+            es = 0.6108 * math.exp(17.27 * tmean / (tmean + 237.3))
+            rh = self.province["rh"] + rng.normal(0, 0.05)
+            rh = max(0.2, min(0.95, rh))
+            vap = es * rh * 10.0
+
+            wind = max(0.5, self.province["wind_base"] + rng.normal(0, 0.8))
+
+            e0, es0, et0 = reference_ET(
+                DAY=day, LAT=self.latitude, ELEV=self.elevation,
+                TMIN=float(tmin), TMAX=float(tmax), IRRAD=float(irrad),
+                VAP=float(vap), WIND=float(wind),
+                ANGSTA=self.angstA, ANGSTB=self.angstB,
+            )
+
+            t = {
+                "DAY": day,
+                "LAT": self.latitude,
+                "LON": self.longitude,
+                "ELEV": self.elevation,
+                "TMAX": float(tmax),
+                "TMIN": float(tmin),
+                "VAP": float(vap),
+                "WIND": float(wind),
+                "RAIN": float(rain) / 10.0,
+                "IRRAD": float(irrad),
+                "E0": float(e0) / 10.0,
+                "ES0": float(es0) / 10.0,
+                "ET0": float(et0) / 10.0,
+                "SNOWDEPTH": 0.0,
+            }
+            wdc = WeatherDataContainer(**t)
+            self._store_WeatherDataContainer(wdc, day)
 
 
-def list_available_crops(grid_no: int = 31031, year: int = 2000) -> list[dict]:
-    """返回指定 grid 与年份有作物日历的作物列表。"""
-    conn = get_db_conn()
-    c = conn.cursor()
-    rows = c.execute(
-        "SELECT crop_no FROM crop_calendar WHERE grid_no=? AND year=? ORDER BY crop_no",
-        (grid_no, year),
-    ).fetchall()
-    conn.close()
-    result = []
-    for r in rows:
-        meta = CROP_META.get(r.crop_no)
-        if meta:
-            result.append({"crop_no": r.crop_no, **meta})
-    return result
+# ─── 农事管理构造 ───────────────────────────────────────────────────────────
+def build_agromanagement(crop_no: int, province: str, year: int):
+    meta = CROP_META[crop_no]
+    crop_name = meta["en"]
+    cal = CROP_CALENDAR_CN[meta["name"]][province]
+    sm, sd = cal["start"]
+    em, ed = cal["end"]
+
+    start_date = datetime.date(year, sm, sd)
+    end_date = datetime.date(year, em, ed)
+    if end_date < start_date:
+        end_date = datetime.date(year + 1, em, ed)
+        campaign_start = datetime.date(year, 1, 1)
+    else:
+        campaign_start = datetime.date(year, 1, 1)
+
+    return [{
+        campaign_start: {
+            "CropCalendar": {
+                "crop_name": crop_name,
+                "variety_name": f"{crop_name}_{province}_{year}",
+                "crop_start_date": start_date,
+                "crop_start_type": "emergence",
+                "crop_end_date": end_date,
+                "crop_end_type": "earliest",
+                "max_duration": 365,
+            },
+            "TimedEvents": None,
+            "StateEvents": None,
+        }
+    }]
+
+
+# ─── 公开接口 ───────────────────────────────────────────────────────────────
+def list_available_provinces() -> list[str]:
+    return list(PROVINCES.keys())
+
+
+def list_available_crops(province: str) -> list[dict]:
+    return [{"crop_no": c, **CROP_META[c]} for c in PROVINCE_CROPS.get(province, [])]
 
 
 def run_wofost(
-    grid_no: int = 31031,
-    crop_no: int = 1,
-    year: int = 2000,
+    province: str,
+    crop_no: int,
+    year: int,
     mode: Literal["pp", "wlp"] = "wlp",
 ) -> dict:
-    """
-    运行 WOFOST 模拟，返回包含输出时间序列与关键指标的字典。
-    """
+    agromanagement = build_agromanagement(crop_no, province, year)
+
+    # 确定气象起止日期（覆盖整个生育期 + 前后缓冲）
+    campaign = agromanagement[0]
+    campaign_date = list(campaign.keys())[0]
+    cc = campaign[campaign_date]["CropCalendar"]
+    start = campaign_date - datetime.timedelta(days=30)
+    end = cc["crop_end_date"] + datetime.timedelta(days=30)
+
+    seed = year * 1000 + hash(province) % 100000
+    wdp = SyntheticWeatherDataProvider(province, start, end, seed)
+
     conn = get_db_conn()
     try:
-        agromanagement = AgroManagementDataProvider(conn, grid_no, crop_no, year)
-        sited = fetch_sitedata(conn, grid_no, year)
-        cropd = fetch_cropdata(conn, grid_no, year, crop_no)
-        soild = fetch_soildata(conn, grid_no)
-        parvalues = ParameterProvider(sitedata=sited, soildata=soild, cropdata=cropd)
-        wdp = GridWeatherDataProvider(conn, grid_no=grid_no)
-
-        if mode == "pp":
-            wofsim = Wofost72_PP(parvalues, wdp, agromanagement)
-        else:
-            wofsim = Wofost72_WLP_CWB(parvalues, wdp, agromanagement)
-
-        wofsim.run_till_terminate()
-        output = wofsim.get_output()
+        # 作物/土壤参数复用 PCSE Demo DB（生理参数通用）
+        cropd = fetch_cropdata(conn, 31031, 2000, crop_no)
+        soild = fetch_soildata(conn, 31031)
+        parvalues = ParameterProvider(sitedata=SITEDATA, soildata=soild, cropdata=cropd)
     finally:
         conn.close()
 
+    if mode == "pp":
+        wofsim = Wofost72_PP(parvalues, wdp, agromanagement)
+    else:
+        wofsim = Wofost72_WLP_CWB(parvalues, wdp, agromanagement)
+
+    wofsim.run_till_terminate()
+    output = wofsim.get_output()
     df = pd.DataFrame(output)
     if not df.empty and "day" in df.columns:
         df = df.set_index("day")
 
-    # 关键指标
     if not df.empty:
         last = df.iloc[-1]
         max_lai = df["LAI"].max() if "LAI" in df.columns else None
@@ -126,28 +343,27 @@ def run_wofost(
     else:
         max_lai = tagp = twso = sm_end = dvs_end = duration = None
 
-    # 生育期里程碑（基于 DVS）
     milestones = {}
+    crop_start = cc["crop_start_date"]
+    crop_end = cc["crop_end_date"]
     if not df.empty and "DVS" in df.columns:
         dvs_series = df["DVS"]
-        # 出苗/开始生长 DVS ~0（实际在 calendar 开始日）
-        # 开花 DVS ~1.0
-        # 成熟 DVS ~2.0
         flowering = dvs_series[dvs_series >= 1.0]
         if not flowering.empty:
             milestones["flowering"] = flowering.index[0]
         maturity = dvs_series[dvs_series >= 2.0]
         if not maturity.empty:
             milestones["maturity"] = maturity.index[0]
-        # 开始日
-        milestones["start"] = df.index[0]
-        milestones["end"] = df.index[-1]
+        milestones["start"] = crop_start
+        milestones["end"] = df.index[-1] if df.index[-1] <= crop_end else crop_end
+
+    actual_duration = (milestones.get("end", crop_end) - crop_start).days + 1 if milestones else None
 
     meta = CROP_META.get(crop_no, {"name": "未知作物", "emoji": "🌱", "color": "#888"})
 
     return {
         "meta": {
-            "grid_no": grid_no,
+            "province": province,
             "crop_no": crop_no,
             "year": year,
             "mode": mode,
@@ -156,30 +372,27 @@ def run_wofost(
         },
         "df": df,
         "summary": {
-            "tagp": tagp,  # 总地上生物量 kg/ha
-            "twso": twso,  # 经济产量 kg/ha
+            "tagp": tagp,
+            "twso": twso,
             "max_lai": max_lai,
             "sm_end": sm_end,
             "dvs_end": dvs_end,
-            "duration": duration,
+            "duration": actual_duration,
             "milestones": milestones,
         },
     }
 
 
 def run_multi_crop(
-    grid_no: int = 31031,
-    year: int = 2000,
+    province: str,
+    year: int,
     mode: Literal["pp", "wlp"] = "wlp",
 ) -> list[dict]:
-    """
-    对指定年份所有可用作物运行模拟，返回各作物汇总结果列表（按 TWSO 排序）。
-    """
-    crops = list_available_crops(grid_no, year)
+    crops = list_available_crops(province)
     results = []
     for c in crops:
         try:
-            res = run_wofost(grid_no=grid_no, crop_no=c["crop_no"], year=year, mode=mode)
+            res = run_wofost(province=province, crop_no=c["crop_no"], year=year, mode=mode)
             results.append({
                 "crop_no": c["crop_no"],
                 "name": c["name"],
@@ -191,26 +404,23 @@ def run_multi_crop(
                 "duration": res["summary"]["duration"],
             })
         except Exception:
-            # 某些作物可能因数据缺失导致模拟失败，静默跳过
             continue
     results.sort(key=lambda x: (x["twso"] if x["twso"] is not None else -1), reverse=True)
     return results
 
 
 def run_multi_year(
-    grid_no: int = 31031,
-    crop_no: int = 1,
+    province: str,
+    crop_no: int,
     mode: Literal["pp", "wlp"] = "wlp",
+    years: list[int] | None = None,
 ) -> list[dict]:
-    """
-    对指定作物在所有可用年份运行模拟，返回各年份汇总结果列表。
-    """
-    years = list_available_years(grid_no)
+    if years is None:
+        years = list(range(2019, 2024))
     results = []
-    meta = CROP_META.get(crop_no, {"name": "未知作物", "emoji": "🌱", "color": "#888"})
     for year in years:
         try:
-            res = run_wofost(grid_no=grid_no, crop_no=crop_no, year=year, mode=mode)
+            res = run_wofost(province=province, crop_no=crop_no, year=year, mode=mode)
             results.append({
                 "year": year,
                 "tagp": res["summary"]["tagp"],