bd3d73d140
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
279 lines
10 KiB
Python
279 lines
10 KiB
Python
"""
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麦麦智农 - 基于 PCSE/WOFOST 的作物生长模拟平台
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"""
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import streamlit as st
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import plotly.graph_objects as go
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from simulator import (
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run_wofost,
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run_multi_crop,
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list_available_provinces,
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list_available_crops,
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CROP_META,
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)
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st.set_page_config(
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page_title="麦麦智农",
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page_icon="🌾",
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layout="wide",
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initial_sidebar_state="expanded",
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)
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# ─── Sidebar ────────────────────────────────────────────────────────────────
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with st.sidebar:
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st.header("🌾 麦麦智农")
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st.caption("基于 PCSE/WOFOST 的真实作物模拟")
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st.divider()
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province = st.selectbox("省份", list_available_provinces())
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year = st.selectbox("年份", [2019, 2020, 2021, 2022, 2023], index=3)
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crops = list_available_crops(province)
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crop_options = {c["crop_no"]: f"{c['emoji']} {c['name']}" for c in crops}
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selected_crop_no = st.selectbox(
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"作物", list(crop_options.keys()), format_func=lambda k: crop_options[k]
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)
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mode = st.radio(
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"生产模式",
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["pp", "wlp"],
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format_func=lambda m: {"pp": "潜在生产 (PP)", "wlp": "水分限制生产 (WLP)"}[m],
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)
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area = st.number_input(
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"种植面积 (公顷)", min_value=0.1, max_value=10000.0, value=100.0, step=10.0
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)
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# ─── Run Simulation ─────────────────────────────────────────────────────────
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@st.cache_data(show_spinner=False)
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def cached_run(province, crop_no, year, mode):
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return run_wofost(province=province, crop_no=crop_no, year=year, mode=mode)
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@st.cache_data(show_spinner=False)
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def cached_multi_crop(province, year, mode):
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return run_multi_crop(province=province, year=year, mode=mode)
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with st.spinner("正在运行 WOFOST 作物模拟，请稍候..."):
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result = cached_run(province, selected_crop_no, year, mode)
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multi_crop_result = cached_multi_crop(province, year, mode)
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meta = result["meta"]
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summary = result["summary"]
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df = result["df"]
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crop_info = CROP_META.get(selected_crop_no, {"name": "未知作物", "emoji": "🌱", "color": "#888"})
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twso = summary["twso"] if summary["twso"] is not None else 0.0
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tagp = summary["tagp"] if summary["tagp"] is not None else 0.0
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max_lai = summary["max_lai"] if summary["max_lai"] is not None else 0.0
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total_twso_tons = twso * area / 1000.0
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# ─── Header ─────────────────────────────────────────────────────────────────
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st.title(f"{crop_info['emoji']} {crop_info['name']} 生长模拟")
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st.caption(f"{province} · {year} 年 · {meta['mode_label']} · 基于 WOFOST 7.2 真实作物模型")
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# ─── KPIs ───────────────────────────────────────────────────────────────────
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k1, k2, k3, k4 = st.columns(4)
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k1.metric("经济产量 (TWSO)", f"{twso:,.0f} kg/ha")
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k2.metric("总生物量 (TAGP)", f"{tagp:,.0f} kg/ha")
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k3.metric("最大叶面积指数 (LAI)", f"{max_lai:.2f}")
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k4.metric(f"{area:.0f} 公顷总产量", f"{total_twso_tons:,.1f} 吨")
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st.divider()
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# ─── Charts Row 1 ───────────────────────────────────────────────────────────
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chart_left, chart_right = st.columns(2)
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with chart_left:
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st.subheader("📈 叶面积指数 (LAI) 动态")
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if not df.empty and "LAI" in df.columns:
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fig_lai = go.Figure()
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fig_lai.add_trace(
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go.Scatter(
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x=df.index,
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y=df["LAI"],
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mode="lines",
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line=dict(color=crop_info["color"], width=2.5),
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fill="tozeroy",
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name="LAI",
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)
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)
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ms = summary.get("milestones", {})
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if "flowering" in ms:
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fig_lai.add_vline(
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x=ms["flowering"],
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line=dict(color="#d4a574", width=1.5, dash="dot"),
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annotation_text="开花",
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annotation_position="top left",
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)
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if "maturity" in ms:
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fig_lai.add_vline(
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x=ms["maturity"],
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line=dict(color="#7c5e42", width=1.5, dash="dash"),
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annotation_text="成熟",
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annotation_position="top right",
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)
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fig_lai.update_layout(
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xaxis_title="日期",
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yaxis_title="LAI",
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margin=dict(t=20, b=36, l=50, r=20),
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height=300,
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showlegend=False,
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)
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st.plotly_chart(fig_lai, use_container_width=True)
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else:
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st.info("暂无 LAI 数据")
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with chart_right:
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st.subheader("📊 生物量与产量积累")
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if not df.empty and "TAGP" in df.columns:
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fig_bio = go.Figure()
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fig_bio.add_trace(
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go.Scatter(
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x=df.index, y=df["TAGP"], mode="lines", name="TAGP", line=dict(color="#7a9e7e", width=2.5)
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)
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)
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if "TWSO" in df.columns:
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fig_bio.add_trace(
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go.Scatter(
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x=df.index,
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y=df["TWSO"],
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mode="lines",
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name="TWSO",
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line=dict(color=crop_info["color"], width=2.5),
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)
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)
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ms = summary.get("milestones", {})
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if "flowering" in ms:
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fig_bio.add_vline(x=ms["flowering"], line=dict(color="#d4a574", width=1.5, dash="dot"))
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if "maturity" in ms:
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fig_bio.add_vline(x=ms["maturity"], line=dict(color="#7c5e42", width=1.5, dash="dash"))
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fig_bio.update_layout(
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xaxis_title="日期",
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yaxis_title="干物质 (kg/ha)",
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margin=dict(t=20, b=36, l=50, r=20),
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height=300,
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legend=dict(orientation="h", y=-0.18),
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)
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st.plotly_chart(fig_bio, use_container_width=True)
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else:
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st.info("暂无生物量数据")
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# ─── Charts Row 2 ───────────────────────────────────────────────────────────
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chart2_left, chart2_right = st.columns(2)
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with chart2_left:
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st.subheader("💧 土壤水分动态")
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if not df.empty and "SM" in df.columns:
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fig_sm = go.Figure()
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fig_sm.add_trace(
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go.Scatter(
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x=df.index,
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y=df["SM"],
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mode="lines",
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line=dict(color="#5a8f9e", width=2.5),
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fill="tozeroy",
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name="SM",
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)
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)
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fig_sm.update_layout(
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xaxis_title="日期",
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yaxis_title="土壤含水量 (cm³/cm³)",
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margin=dict(t=20, b=36, l=50, r=20),
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height=280,
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showlegend=False,
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)
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st.plotly_chart(fig_sm, use_container_width=True)
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else:
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st.info("暂无土壤水分数据")
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with chart2_right:
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st.subheader("🏅 同一年份作物产量对比")
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if multi_crop_result:
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names = [
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f"{CROP_META.get(r['crop_no'], {}).get('emoji', '🌱')} {r['name']}"
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for r in multi_crop_result
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]
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twsos = [r["twso"] if r["twso"] is not None else 0.0 for r in multi_crop_result]
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colors = [CROP_META.get(r["crop_no"], {}).get("color", "#888") for r in multi_crop_result]
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fig_bar = go.Figure()
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fig_bar.add_trace(
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go.Bar(
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x=names,
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y=twsos,
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marker=dict(color=colors, opacity=0.85),
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text=[f"{v:,.0f}" for v in twsos],
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textposition="outside",
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)
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)
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fig_bar.update_layout(
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yaxis_title="经济产量 (kg/ha)",
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margin=dict(t=20, b=30, l=60, r=20),
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height=280,
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showlegend=False,
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)
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st.plotly_chart(fig_bar, use_container_width=True)
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else:
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st.info("暂无对比数据")
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# ─── Advisory Panel ─────────────────────────────────────────────────────────
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st.divider()
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st.subheader("💡 农艺建议与生育期")
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adv1, adv2 = st.columns(2)
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with adv1:
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if twso > 6000:
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st.success(
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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。"
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)
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elif twso > 3000:
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st.info(
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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。"
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)
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else:
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st.warning(
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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。"
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)
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if mode == "wlp":
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if not df.empty and "SM" in df.columns:
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sm_min = df["SM"].min()
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if sm_min < 0.15:
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st.warning(
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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。"
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)
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else:
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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。")
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else:
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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。")
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if max_lai > 5:
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st.success(f"最大 LAI 达到 {max_lai:.2f}，冠层覆盖充分，光能截获效率高。")
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elif max_lai < 2:
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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。")
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with adv2:
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ms = summary.get("milestones", {})
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if ms:
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st.info(
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f"""
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**生育期里程碑**
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- 播种/出苗：{ms.get('start', '—')}
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- 开花期：{ms.get('flowering', '—')}
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- 成熟期：{ms.get('maturity', '—')}
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- 收获/结束：{ms.get('end', '—')}
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- 生育期天数：{summary.get('duration', '—')} 天
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"""
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)
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else:
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st.info("暂无生育期数据")
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st.divider()
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st.caption("麦麦智农 · 基于 PCSE/WOFOST 真实作物生长模型 · 结果仅供参考")
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