feat: 初始化麦麦智农作物生长模拟平台

- 基于 PCSE/WOFOST 构建作物生长模拟平台 - 新增 Streamlit 可视化应用（app.py）与模拟引擎（simulator.py） - 支持潜在生产（PP）与水分限制生产（WLP）两种模拟模式 - 支持冬小麦、玉米、春大麦、马铃薯、冬油菜、向日葵 6 种作物 - 提供 LAI 动态、生物量积累、土壤水分、产量对比等可视化图表 - 新增 pyproject.toml、justfile、Dockerfile 等工程配置 - 完善 README.md 项目文档与 .gitignore 忽略规则
2026-04-14 14:46:35 +08:00
parent 0edbaf1db5
commit e4543ce7bd
9 changed files with 1826 additions and 2 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1,26 @@
+# Python
+__pycache__/
+*.py[cod]
+*.egg-info/
+dist/
+build/
+
+# Virtual environment
+.venv/
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# Streamlit
+.streamlit_cache/
+
+# Ruff
+.ruff_cache/
+
+# OS
+.DS_Store
+Thumbs.db
+/.doc/
--- a/43
+++ b/43
@@ -0,0 +1,43 @@
+# 使用 uv 官方镜像作为基础镜像
+FROM 172.16.102.3:30648/astral-sh/uv:python3.14-bookworm
+
+WORKDIR /app
+
+# 配置 apt 使用阿里云镜像源
+RUN sed -i 's/httpredir.debian.org/mirrors.aliyun.com/g' /etc/apt/sources.list.d/debian.sources
+
+# 安装系统依赖
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# 复制项目配置文件
+COPY pyproject.toml justfile ./
+
+# 配置 uv 使用阿里云镜像源
+ENV UV_INDEX_URL=https://mirrors.aliyun.com/pypi/simple/ \
+    UV_HTTP_TIMEOUT=300
+
+# 安装 Python 依赖
+RUN uv sync --no-dev
+
+# 复制应用代码
+COPY . .
+
+# 暴露端口
+EXPOSE 8000
+
+# Streamlit 环境变量
+ENV STREAMLIT_SERVER_PORT=8000 \
+    STREAMLIT_SERVER_ADDRESS=0.0.0.0 \
+    STREAMLIT_SERVER_ENABLE_XSRF_PROTECTION=false \
+    STREAMLIT_SERVER_ENABLE_CORS=false \
+    STREAMLIT_SERVER_HEADLESS=true \
+    STREAMLIT_BROWSER_GATHER_USAGE_STATS=false
+
+# 健康检查
+HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
+    CMD curl -f http://localhost:8000/_stcore/health || exit 1
+
+# 启动应用
+CMD ["uv", "run", "streamlit", "run", "app.py", "--server.port=8000", "--server.address=0.0.0.0"]
--- a/README.md
+++ b/README.md
@@ -1,3 +1,58 @@
-# maimai-pcse
+# 麦麦智农 (maimai-pcse)

-maimai-pcse
+基于 [PCSE](https://github.com/ajwdewit/pcse) / WOFOST 作物生长模型的智能种植模拟平台。通过真实的作物生理生态模拟，帮助农户与农技人员评估不同作物在特定气候与土壤条件下的生长表现与产量潜力。
+
+## 功能特性
+
+- 真实作物模拟：基于 WOFOST 7.2 潜在生产（PP）与水分限制生产（WLP）模型
+- 多作物支持：冬小麦、春大麦、马铃薯、冬油菜、玉米、向日葵
+- 生长曲线可视化：LAI、生物量、籽粒产量、土壤水分动态
+- 产量对比分析：同一年份多作物产量横向对比
+- 农艺建议：基于模拟结果的关键指标解读与环境匹配提示
+
+## 技术栈
+
+- Python 3.12+
+- Streamlit
+- Plotly
+- Pandas / NumPy
+- PCSE (Python Crop Simulation Environment)
+
+## 快速开始
+
+### 使用 uv（推荐）
+
+```bash
+# 安装依赖
+uv sync
+
+# 运行应用
+uv run streamlit run app.py
+```
+
+### 使用 just
+
+```bash
+just run
+```
+
+## 项目结构
+
+```
+maimai-pcse/
+├── app.py           # Streamlit 主应用
+├── simulator.py     # PCSE 模拟封装
+├── main.py          # 入口文件
+├── pyproject.toml   # 项目配置
+├── justfile         # 任务自动化
+├── Dockerfile       # Docker 配置
+└── README.md        # 项目文档
+```
+
+## 数据说明
+
+应用内置 PCSE Demo 数据库（西班牙 grid 31031，1999–2001 年气象数据），开箱即可运行真实作物模拟，无需额外准备气象与土壤数据。
+
+## 许可证
+
+MIT License
--- a/app.py
+++ b/app.py
@@ -0,0 +1,444 @@
+"""
+麦麦智农 - 基于 PCSE/WOFOST 的作物生长模拟平台
+"""
+
+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
+
+# ─── Page Config ────────────────────────────────────────────────────────────
+st.set_page_config(
+    page_title="麦麦智农",
+    page_icon="🌾",
+    layout="wide",
+    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 ──────────────────────────────────────────────────────────
+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.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)
+
+    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)
+    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])
+
+    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)
+
+
+# ─── 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)
+
+
+@st.cache_data(show_spinner=False)
+def cached_multi_crop(year, mode):
+    return run_multi_crop(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)
+
+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)
+
+st.markdown("<br>", unsafe_allow_html=True)
+
+# KPI row
+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)
+
+st.markdown("<br>", unsafe_allow_html=True)
+
+# ─── 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)
+    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=crop_info["color"] + "1A",
+            name="LAI",
+        ))
+        # 添加生育期标记
+        ms = summary.get("milestones", {})
+        if "flowering" in ms:
+            fig_lai.add_vline(x=ms["flowering"], line=dict(color="#d4a574", width=1.5, dash="dot"),
+                              annotation_text="开花", annotation_font_color="#7c5e42",
+                              annotation_position="top left")
+        if "maturity" in ms:
+            fig_lai.add_vline(x=ms["maturity"], line=dict(color="#7c5e42", width=1.5, dash="dash"),
+                              annotation_text="成熟", annotation_font_color="#7c5e42",
+                              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,
+            showlegend=False,
+        )
+        st.plotly_chart(fig_lai, use_container_width=True)
+    else:
+        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)
+    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",
+        ))
+        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",
+            ))
+        ms = summary.get("milestones", {})
+        if "flowering" in ms:
+            fig_bio.add_vline(x=ms["flowering"], line=dict(color="#d4a574", width=1.5, dash="dot"))
+        if "maturity" in ms:
+            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)),
+        )
+        st.plotly_chart(fig_bio, use_container_width=True)
+    else:
+        st.info("暂无生物量数据")
+
+# ─── 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)
+    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.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,
+            showlegend=False,
+        )
+        st.plotly_chart(fig_sm, use_container_width=True)
+    else:
+        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)
+    if 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.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,
+            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)
+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。"))
+    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。"))
+    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。"))
+
+    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。"))
+            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。"))
+    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。"))
+
+    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。"))
+    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)
+
+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)
+
+# ─── 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)
--- a/25
+++ b/25
@@ -0,0 +1,25 @@
+# Justfile for maimai-pcse 麦麦智农
+
+# Default: show available commands
+default:
+    just --list
+
+# Run the Streamlit app
+run:
+    uv run streamlit run app.py
+
+# Install dependencies
+install:
+    uv sync
+
+# Format code with ruff (if available)
+format:
+    uv run ruff format .
+
+# Check code
+check:
+    uv run ruff check .
+
+# Clean caches
+clean:
+    rm -rf __pycache__ .ruff_cache .streamlit
--- a/main.py
+++ b/main.py
@@ -0,0 +1,6 @@
+def main():
+    print("欢迎使用麦麦智农！请运行 `just run` 或 `uv run streamlit run app.py` 启动应用。")
+
+
+if __name__ == "__main__":
+    main()
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -0,0 +1,17 @@
+[project]
+name = "maimai-pcse"
+version = "0.1.0"
+description = "麦麦智农 - 基于 PCSE/WOFOST 作物生长模型的智能种植模拟平台"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "streamlit>=1.52.0",
+    "plotly>=6.5.0",
+    "pandas>=2.0.0",
+    "numpy>=2.0.0",
+    "pcse @ file:///job/pcse",
+]
+
+[[tool.uv.index]]
+url = "https://mirrors.aliyun.com/pypi/simple"
+default = true
--- a/simulator.py
+++ b/simulator.py
@@ -0,0 +1,223 @@
+"""
+麦麦智农 - PCSE 模拟引擎封装
+基于 WOFOST 7.2 潜在生产（PP）与水分限制生产（WLP）模型
+"""
+
+import sqlite3
+from collections import namedtuple
+from typing import Literal
+
+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,
+)
+
+
+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
+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"},
+    3: {"name": "春大麦", "emoji": "🌿", "color": "#9cb85f", "en": "SPRING BARLEY"},
+    7: {"name": "马铃薯", "emoji": "🥔", "color": "#b8a88a", "en": "POTATO"},
+    10: {"name": "冬油菜", "emoji": "🌻", "color": "#d97836", "en": "WINTER RAPESEED"},
+    11: {"name": "向日葵", "emoji": "🌻", "color": "#f4c430", "en": "SUNFLOWER"},
+}
+
+MODE_LABELS = {
+    "pp": "潜在生产",
+    "wlp": "水分限制生产",
+}
+
+
+def get_db_conn():
+    conn = sqlite3.connect(DB_PATH)
+    conn.row_factory = namedtuple_factory
+    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]
+
+
+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
+
+
+def run_wofost(
+    grid_no: int = 31031,
+    crop_no: int = 1,
+    year: int = 2000,
+    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。
+    """
+    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()
+    finally:
+        conn.close()
+
+    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
+        tagp = last.get("TAGP")
+        twso = last.get("TWSO")
+        sm_end = last.get("SM")
+        dvs_end = last.get("DVS")
+        duration = len(df)
+    else:
+        max_lai = tagp = twso = sm_end = dvs_end = duration = None
+
+    # 生育期里程碑（基于 DVS）
+    milestones = {}
+    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]
+
+    meta = CROP_META.get(crop_no, {"name": "未知作物", "emoji": "🌱", "color": "#888"})
+
+    return {
+        "meta": {
+            "grid_no": grid_no,
+            "crop_no": crop_no,
+            "year": year,
+            "mode": mode,
+            **meta,
+            "mode_label": MODE_LABELS.get(mode, mode),
+        },
+        "df": df,
+        "summary": {
+            "tagp": tagp,  # 总地上生物量 kg/ha
+            "twso": twso,  # 经济产量 kg/ha
+            "max_lai": max_lai,
+            "sm_end": sm_end,
+            "dvs_end": dvs_end,
+            "duration": duration,
+            "milestones": milestones,
+        },
+    }
+
+
+def run_multi_crop(
+    grid_no: int = 31031,
+    year: int = 2000,
+    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)
+    results = []
+    for c in crops:
+        try:
+            res = run_wofost(grid_no=grid_no, crop_no=c["crop_no"], year=year, mode=mode)
+            results.append({
+                "crop_no": c["crop_no"],
+                "name": c["name"],
+                "emoji": c["emoji"],
+                "color": c["color"],
+                "tagp": res["summary"]["tagp"],
+                "twso": res["summary"]["twso"],
+                "max_lai": res["summary"]["max_lai"],
+                "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,
+    mode: Literal["pp", "wlp"] = "wlp",
+) -> 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)
+    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)
+            results.append({
+                "year": year,
+                "tagp": res["summary"]["tagp"],
+                "twso": res["summary"]["twso"],
+                "max_lai": res["summary"]["max_lai"],
+                "duration": res["summary"]["duration"],
+            })
+        except Exception:
+            continue
+    return results
--- a/uv.lock
+++ b/uv.lock
@@ -0,0 +1,985 @@
+version = 1
+revision = 3
+requires-python = ">=3.12"
+resolution-markers = [
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+    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.14' and sys_platform == 'win32'",
+    "python_full_version < '3.14' and sys_platform == 'emscripten'",
+    "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
+
+[[package]]
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