""" 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(""" """, unsafe_allow_html=True) # ─── Sidebar Inputs ────────────────────────────────────────────────────────── with st.sidebar: st.markdown('', unsafe_allow_html=True) st.markdown('', unsafe_allow_html=True) st.markdown("
", unsafe_allow_html=True) st.markdown('
🗺 模拟地点
', unsafe_allow_html=True) st.markdown("""
西班牙 · 埃斯特雷马杜拉
Grid 31031 (37.64°N, 6.09°W)
""", unsafe_allow_html=True) st.markdown('
📅 模拟参数
', 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('
🌱 种植规模
', 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"""
麦麦智农 · {crop_info['emoji']} {crop_info['name']} 生长模拟
{meta['year']} 年 · {meta['mode_label']} · 基于 WOFOST 7.2 真实作物模型
""", unsafe_allow_html=True) st.markdown("
", unsafe_allow_html=True) # KPI row k1, k2, k3, k4 = st.columns(4) with k1: st.markdown(f"""
{twso:,.0f}
kg / 公顷
🌾 经济产量 (TWSO)
""", unsafe_allow_html=True) with k2: st.markdown(f"""
{tagp:,.0f}
kg / 公顷
🌿 总生物量 (TAGP)
""", unsafe_allow_html=True) with k3: st.markdown(f"""
{max_lai:.2f}
最大叶面积指数
☘️ LAI max
""", unsafe_allow_html=True) with k4: st.markdown(f"""
{total_twso_tons:,.1f}
吨 / 总产量
📦 {area:.0f} 公顷总产
""", unsafe_allow_html=True) st.markdown("
", unsafe_allow_html=True) # ─── Charts Row 1 ──────────────────────────────────────────────────────────── chart_left, chart_right = st.columns(2) with chart_left: st.markdown('
📈 叶面积指数 (LAI) 动态
', 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('
📊 生物量与产量积累
', 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('
💧 土壤水分动态
', 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('
🏅 同一年份作物产量对比
', 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('
💡 农艺建议与生育期
', 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'
{msg}
', unsafe_allow_html=True) with adv2: ms = summary.get("milestones", {}) if ms: st.markdown(f"""
生育期里程碑
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
""", unsafe_allow_html=True) else: st.markdown('
暂无生育期数据
', unsafe_allow_html=True) # ─── Footer ─────────────────────────────────────────────────────────────────── st.markdown("
", unsafe_allow_html=True) st.markdown("""
麦麦智农 · 基于 PCSE/WOFOST 真实作物生长模型 · 结果仅供参考
""", unsafe_allow_html=True)