使用python与streamlit构建的空间微生物分析

我们尝试使用python与streamlit实现了一个交互式的空间微生物消杀效果分析的web应用示例，用于分析微生物在不同环境下的行为，并评估各种消毒方法的效果。

环境与微生物行为分析，用户可以选择地球或太空环境，并输入温度、湿度等参数，系统会模拟微生物（如大肠杆菌、霉菌等）的生长速率，预测其在无消毒处理时的峰值浓度。

消毒方法评估，提供多种消毒方法（如气体等离子体、臭氧、湿巾等），计算每种方法在不同环境下的效率（百分比），并生成图表对比。

安全评估与推荐，根据用户输入的环境条件和选择的消毒方法，显示峰值浓度是否超出安全标准（如空间站的1000 CFU/m³），并推荐适合该环境的消毒方案。

import streamlit as st
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib as mpl
from matplotlib.font_manager import FontProperties
import sys
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots# ===== 初始化设置 =====
st.set_page_config(page_title="微生物消杀模拟系统",page_icon="🦠",layout="wide",initial_sidebar_state="expanded"
)# ===== 设置字体支持 =====
try:font_path = 'C:/Windows/Fonts/arialuni.ttf'  # 优先选择支持特殊字符的字体if sys.platform == "darwin":  # macOSfont_path = '/System/Library/Fonts/PingFang.ttc'elif sys.platform == "linux":  # Linuxfont_path = '/usr/share/fonts/truetype/wqy/wqy-microhei.ttc'font = FontProperties(fname=font_path)plt.rcParams['font.family'] = font.get_name()plt.rcParams['axes.unicode_minus'] = False
except Exception as e:st.warning(f"字体设置异常: {str(e)}，使用默认字体")# ===== 环境类型设置 =====
ENVIRONMENT_TYPES = {"地球环境": {"gravity": 1.0,"humidity_range": (30, 90),"temperature_range": (10, 40),"pressure": 1.0,"radiation": "低"},"太空环境": {"gravity": 0.01,"humidity_range": (10, 50),  # 太空湿度范围较窄"temperature_range": (15, 35),  # 太空温度范围较窄"pressure": 0.7,"radiation": "高"}
}# ===== 微生物种类数据库 =====
MICROBE_SPECIES = {# 细菌"大肠杆菌": {"type": "细菌", "growth_rate": 0.25, "resistance": 0.2, "radiation_resist": 0.1},"金黄色葡萄球菌": {"type": "细菌", "growth_rate": 0.22, "resistance": 0.3, "radiation_resist": 0.15},"沙门氏菌": {"type": "细菌", "growth_rate": 0.24, "resistance": 0.25, "radiation_resist": 0.12},# 真菌"霉菌": {"type": "真菌", "growth_rate": 0.18, "resistance": 0.4, "radiation_resist": 0.25},"酵母菌": {"type": "真菌", "growth_rate": 0.16, "resistance": 0.35, "radiation_resist": 0.2},"黑曲霉": {"type": "真菌", "growth_rate": 0.17, "resistance": 0.45, "radiation_resist": 0.3},# 孢子"枯草芽孢杆菌": {"type": "孢子", "growth_rate": 0.12, "resistance": 0.65, "radiation_resist": 0.6},"艰难梭菌": {"type": "孢子", "growth_rate": 0.1, "resistance": 0.7, "radiation_resist": 0.65},"炭疽杆菌": {"type": "孢子", "growth_rate": 0.11, "resistance": 0.75, "radiation_resist": 0.7}
}# ===== 消杀方法数据库 =====
DISINFECTION_METHODS = {"紫外线照射(254nm)": {"efficiency": {"地球": 0.02, "太空": 0.015},"limitations": "无法穿透物体阴影","cost": "中","safety": "安全"},"过氧化氢蒸汽": {"efficiency": {"地球": 0.03, "太空": 0.025},"limitations": "高湿度需求，可能腐蚀设备","cost": "高","safety": "中等"},"气体等离子体": {"efficiency": {"地球": 0.025, "太空": 0.022},"limitations": "可能产生有害副产物","cost": "高","safety": "中等"},"光催化氧化(TiO₂)": {"efficiency": {"地球": 0.015, "太空": 0.01},"limitations": "需要持续光照","cost": "中","safety": "安全"},"超临界CO₂": {"efficiency": {"地球": 0.035, "太空": 0.03},"limitations": "需要高压设备","cost": "很高","safety": "安全"},"臭氧(O₃)": {"efficiency": {"地球": 0.027, "太空": 0.02},"limitations": "高浓度对人体有害","cost": "中","safety": "低"},"环氧乙烷(EtO)": {"efficiency": {"地球": 0.032, "太空": 0.025},"limitations": "易燃易爆，有残留毒性","cost": "高","safety": "低"},"季铵盐湿巾": {"efficiency": {"地球": 0.012, "太空": 0.005},"limitations": "难以覆盖所有表面","cost": "低","safety": "安全"}
}# ===== 侧边栏参数设置 =====
st.sidebar.header("环境设置")
environment = st.sidebar.selectbox("环境类型", list(ENVIRONMENT_TYPES.keys()))
env_params = ENVIRONMENT_TYPES[environment]st.sidebar.subheader(f"{environment}参数")
temp = st.sidebar.slider("温度(℃)", min_value=env_params["temperature_range"][0],max_value=env_params["temperature_range"][1],value=25
)
humidity = st.sidebar.slider("湿度(%)", min_value=env_params["humidity_range"][0],max_value=env_params["humidity_range"][1],value=45 if environment == "太空环境" else 60
)
volume = st.sidebar.selectbox("空间体积(m³)", [10, 50, 100, 200, 500], index=2)st.sidebar.header("微生物参数")
microbe_species = st.sidebar.selectbox("微生物种类", options=list(MICROBE_SPECIES.keys()),index=0
)# 获取微生物参数
microbe_params = MICROBE_SPECIES[microbe_species]
st.sidebar.markdown(f"**类型**: {microbe_params['type']}")
st.sidebar.markdown(f"**生长速率**: {microbe_params['growth_rate']:.2f}/h")
st.sidebar.markdown(f"**抗性**: {microbe_params['resistance']*100:.0f}%")
st.sidebar.markdown(f"**辐射抵抗**: {microbe_params['radiation_resist']*100:.0f}%")init_concentration = st.sidebar.number_input("初始浓度(CFU/m³)", min_value=100, max_value=10000, value=1000
)st.sidebar.header("消杀方案")
selected_methods = st.sidebar.multiselect("选择消杀方法(可多选)", options=list(DISINFECTION_METHODS.keys()),default=["气体等离子体", "超临界CO₂"]
)exposure_time = st.sidebar.slider("作用时间(分钟)", 1, 120, 30)# ===== 核心算法 =====
def growth_rate(temp, humidity, microbe_params, env_params):"""微生物生长速率模型 - 基于文档2.1-2.11节"""base_rate = microbe_params["growth_rate"]# 温度影响系数 - 25℃为最适温度temp_factor = np.exp(-0.5 * ((temp - 25) / 10)**2)# 湿度影响系数if environment == "太空环境":# 太空环境下湿度影响减小humid_factor = 0.7 + 0.3 * ((humidity - 10) / 40)  # 适应太空的窄湿度范围else:humid_factor = 0.5 + 0.5 * (humidity / 100)# 重力影响系数gravity_factor = 1.2 - 0.2 * env_params["gravity"]  # 微重力环境下生长更快# 辐射影响系数（针对太空环境）radiation_factor = 1.0 if environment == "地球环境" else 0.9 + microbe_params["radiation_resist"] * 0.2return base_rate * temp_factor * humid_factor * gravity_factor * radiation_factordef disinfection_efficiency(method, time, microbe_params, env_params):"""消杀效率模型 - 基于文档3.1-3.8节"""method_params = DISINFECTION_METHODS[method]# 基础效率base_efficiency = method_params["efficiency"]["太空" if environment == "太空环境" else "地球"]efficiency = base_efficiency * time# 微生物抗性影响efficiency *= (1 - microbe_params["resistance"])# 环境因素影响if environment == "太空环境":# 太空环境下某些方法效率降低if method in ["季铵盐湿巾", "臭氧(O₃)"]:efficiency *= 0.8  # 太空环境下湿巾效果变差elif method in ["光催化氧化(TiO₂)"]:efficiency *= 0.9  # 光催化在太空环境下略有降低# 效率上限return min(0.95, efficiency)  # 最大效率95%# ===== 动态模拟 =====
# 生成时间序列
hours = np.linspace(0, 48, 200)  # 扩展到48小时
growth_rate_val = growth_rate(temp, humidity, microbe_params, env_params)
growth_curve = init_concentration * np.exp(growth_rate_val * hours)# 计算消杀时间点
disinfection_idx = min(int(exposure_time / (24*60) * len(hours)), len(hours)-1)# ===== 主界面布局 =====
st.title(f"密闭空间微生物消杀效果模拟 - {environment}")
st.caption("基于《密闭空间微生物消杀方法研究进展》戴荣继等（北京理工大学）")# 创建选项卡
tab1, tab2, tab3 = st.tabs(["生长与消杀曲线", "环境对比分析", "方法对比矩阵"])with tab1:# === 图1：微生物生长与消杀曲线 ===st.subheader(f"{microbe_species}在不同消杀方案下的浓度变化")fig1 = go.Figure()fig1.add_trace(go.Scatter(x=hours, y=growth_curve, mode='lines',name='自然生长',line=dict(color='blue', width=2)))# 添加各种消杀方案的曲线colors = px.colors.qualitative.Plotlyfor i, method in enumerate(selected_methods):disinfection_eff = disinfection_efficiency(method, exposure_time, microbe_params, env_params)# 消杀后曲线post_disinfection = growth_curve.copy()post_disinfection[disinfection_idx:] = (growth_curve[disinfection_idx] * (1 - disinfection_eff) * np.exp(growth_rate_val * (hours[disinfection_idx:] - hours[disinfection_idx])))fig1.add_trace(go.Scatter(x=hours, y=post_disinfection, mode='lines',name=f"{method} (效率: {disinfection_eff*100:.1f}%)",line=dict(color=colors[i % len(colors)], width=2, dash='dash')))# 添加消杀时间线fig1.add_vline(x=hours[disinfection_idx], line_dash="dash", line_color="green",annotation_text=f"消杀开始 ({exposure_time}分钟)",annotation_position="top right")# 设置安全标准线fig1.add_hline(y=1000, line_dash="dot", line_color="red",annotation_text="安全浓度上限 (1000 CFU/m³)", annotation_position="bottom right")fig1.update_layout(title=f"微生物生长与消杀模拟曲线",xaxis_title="时间 (小时)",yaxis_title="微生物浓度 (CFU/m³)",legend_title="消杀方法",hovermode="x unified",height=500)st.plotly_chart(fig1, use_container_width=True)# === 效果对比表格 ===st.subheader("消杀效果评估")eff_data = []for method in DISINFECTION_METHODS:eff = disinfection_efficiency(method, exposure_time, microbe_params, env_params)eff_data.append({"消杀方法": method,f"{exposure_time}分钟效率": f"{eff*100:.1f}%","适用场景": DISINFECTION_METHODS[method]["limitations"],"安全性": DISINFECTION_METHODS[method]["safety"],"成本": DISINFECTION_METHODS[method]["cost"]})df_eff = pd.DataFrame(eff_data)df_eff = df_eff.sort_values(f"{exposure_time}分钟效率", ascending=False)# 高亮显示所选方法def highlight_selected(row):if row["消杀方法"] in selected_methods:return ['background-color: #e6f7ff'] * len(row)return [''] * len(row)st.dataframe(df_eff.style.apply(highlight_selected, axis=1),use_container_width=True)with tab2:# === 图2：环境对比分析 ===st.subheader("地球与太空环境微生物行为对比")# 创建子图fig2 = make_subplots(rows=2, cols=2,subplot_titles=("不同温度下生长速率对比","不同湿度下生长速率对比","不同环境消毒效率对比","微生物类别抗性分析"),vertical_spacing=0.15)# 面板1：不同温度下生长速率对比temps = np.linspace(10, 40, 20)earth_growth = [growth_rate(t, 60, MICROBE_SPECIES["大肠杆菌"], ENVIRONMENT_TYPES["地球环境"]) for t in temps]space_growth = [growth_rate(t, 30, MICROBE_SPECIES["大肠杆菌"], ENVIRONMENT_TYPES["太空环境"]) for t in temps]fig2.add_trace(go.Scatter(x=temps, y=earth_growth, name="地球环境-大肠杆菌",line=dict(color='blue', width=2)), row=1, col=1)fig2.add_trace(go.Scatter(x=temps, y=space_growth, name="太空环境-大肠杆菌",line=dict(color='red', width=2, dash='dash')), row=1, col=1)# 面板2：不同湿度下生长速率对比humidities = np.linspace(10, 90, 20)earth_growth_h = [growth_rate(25, h, MICROBE_SPECIES["大肠杆菌"], ENVIRONMENT_TYPES["地球环境"]) for h in humidities]space_growth_h = [growth_rate(25, h, MICROBE_SPECIES["大肠杆菌"], ENVIRONMENT_TYPES["太空环境"]) for h in humidities]fig2.add_trace(go.Scatter(x=humidities, y=earth_growth_h, name="地球环境-大肠杆菌",line=dict(color='blue', width=2), showlegend=False), row=1, col=2)fig2.add_trace(go.Scatter(x=humidities, y=space_growth_h, name="太空环境-大肠杆菌",line=dict(color='red', width=2, dash='dash'), showlegend=False), row=1, col=2)# 面板3：不同环境下消杀效率对比methods_list = list(DISINFECTION_METHODS.keys())earth_eff = [disinfection_efficiency(m, 30, MICROBE_SPECIES["大肠杆菌"], ENVIRONMENT_TYPES["地球环境"])*100 for m in methods_list]space_eff = [disinfection_efficiency(m, 30, MICROBE_SPECIES["大肠杆菌"], ENVIRONMENT_TYPES["太空环境"])*100 for m in methods_list]fig2.add_trace(go.Bar(x=methods_list, y=earth_eff, name="地球环境效率",marker_color='blue'), row=2, col=1)fig2.add_trace(go.Bar(x=methods_list, y=space_eff, name="太空环境效率",marker_color='red'), row=2, col=1)# 面板4：微生物抗性分析microbes = list(MICROBE_SPECIES.keys())growth_rates = [MICROBE_SPECIES[m]["growth_rate"] for m in microbes]resistances = [MICROBE_SPECIES[m]["resistance"] for m in microbes]fig2.add_trace(go.Bar(x=microbes, y=growth_rates, name="生长速率",marker_color='green'), row=2, col=2)fig2.add_trace(go.Bar(x=microbes, y=resistances, name="抗性",marker_color='purple'), row=2, col=2)fig2.update_layout(height=800,title_text="环境与微生物特性综合分析",showlegend=True)st.plotly_chart(fig2, use_container_width=True)with tab3:# === 图3：消杀方法对比矩阵 ===st.subheader("消杀方法综合能力评估")# 准备雷达图数据categories = ['效率', '安全性', '成本效益', '环境适应性', '抗性覆盖']radar_data = []for method in DISINFECTION_METHODS:params = DISINFECTION_METHODS[method]# 评估维度（0-1）efficiency = params["efficiency"]["地球"] * 1.2  # 效率权重较高safety = 0.9 if params["safety"] == "安全" else 0.5cost_effect = 0.8 if params["cost"] == "低" else 0.5env_adapt = 0.85 if environment == "地球环境" else 0.7resist_cover = 0.9 - min(params["efficiency"]["地球"], 0.3)  # 效率越低，抗性覆盖越差values = [efficiency, safety, cost_effect, env_adapt, resist_cover]radar_data.append(go.Scatterpolar(r=values,theta=categories,fill='toself',name=method))fig3 = go.Figure(data=radar_data)fig3.update_layout(polar=dict(radialaxis=dict(visible=True,range=[0, 1.2])),showlegend=True,title="消杀方法能力雷达图",height=600)st.plotly_chart(fig3, use_container_width=True)# 添加方法详情卡片cols = st.columns(2)for i, method in enumerate(selected_methods):with cols[i % 2]:params = DISINFECTION_METHODS[method]eff_earth = params["efficiency"]["地球"] * 100eff_space = params["efficiency"]["太空"] * 100st.markdown(f"### {method}")st.metric("地球环境效率", f"{eff_earth:.1f}%")st.metric("太空环境效率", f"{eff_space:.1f}%")st.markdown(f"**限制**: {params['limitations']}")st.markdown(f"**安全性**: {params['safety']}")st.markdown(f"**成本**: {params['cost']}")# ===== 安全评估 =====
st.divider()
st.header("环境安全评估")
space_std = 1000  # 空间站安全标准 (CFU/m³)# 当前选择的消杀方案评估
cols = st.columns(len(selected_methods)+1)with cols[0]:st.metric("无消杀峰值浓度", f"{max(growth_curve):.0f} CFU/m³", delta=f"超出安全线{(max(growth_curve)/space_std-1)*100:.0f}%" if max(growth_curve) > space_std else "安全范围内",delta_color="inverse")for i, method in enumerate(selected_methods):disinfection_eff = disinfection_efficiency(method, exposure_time, microbe_params, env_params)post_disinfection = growth_curve.copy()post_disinfection[disinfection_idx:] = (growth_curve[disinfection_idx] * (1 - disinfection_eff) * np.exp(growth_rate_val * (hours[disinfection_idx:] - hours[disinfection_idx])))peak_concentration = max(post_disinfection)with cols[i+1]:st.metric(f"{method}峰值", f"{peak_concentration:.0f} CFU/m³", delta=f"效率: {disinfection_eff*100:.1f}%",delta_color="normal")st.progress(disinfection_eff, text=f"消杀效率")# === 方法推荐系统 ===
st.subheader("环境优化方案推荐")
if environment == "太空环境":st.warning("太空环境注意事项:")st.markdown("""- 优先考虑气态消杀方法（等离子体、臭氧）- 避免使用液体消毒剂（湿巾）- 考虑辐射增强消杀效果- 注意微重力对液体分布的影响""")recommended_methods = ["气体等离子体", "臭氧(O₃)", "超临界CO₂"]
else:st.success("地球环境建议:")st.markdown("""- 可使用液体消毒剂（湿巾、过氧化氢溶液）- 组合使用物理和化学方法- 考虑经济性和安全性平衡""")recommended_methods = ["过氧化氢蒸汽", "紫外线照射(254nm)", "季铵盐湿巾"]st.markdown(f"**推荐消杀方法**: {', '.join(recommended_methods)}")