E2B Fragments交通物流：路径规划与供应链管理
项目地址: https://gitcode.com/GitHub_Trending/fr/fragments 概述
在当今数字化时代，交通物流行业面临着前所未有的挑战与机遇。传统物流系统往往存在效率低下、成本高昂、响应迟缓等问题。E2B Fragments作为一个由AI驱动的开源应用构建平台，为交通物流行业提供了革命性的解决方案。
通过结合人工智能的强大能力与安全的代码执行环境，E2B Fragments能够快速生成专业的物流管理应用，实现智能路径规划、实时供应链监控和自动化决策支持。
核心优势
🚀 快速原型开发
- AI驱动生成：基于自然语言描述自动生成完整应用
- 多技术栈支持：Python、Next.js、Vue.js、Streamlit等多种框架
- 实时预览：即时查看生成结果，快速迭代优化
🔒 安全执行环境
- 沙箱隔离：所有代码在安全的E2B环境中执行
- 依赖管理：自动处理Python和npm包依赖
- 企业级安全：符合行业安全标准的数据处理
📊 专业物流功能
- 路径优化算法：集成Dijkstra、A*、遗传算法等
- 实时数据可视化：动态展示物流网络状态
- 预测分析：基于历史数据的智能预测
技术架构
物流应用场景
1. 智能路径规划系统
问题场景：传统物流路径规划往往依赖人工经验，效率低下且容易出错。
E2B解决方案：
import numpy as np
import pandas as pd
import networkx as nx
from geopy.distance import geodesic
import streamlit as st
class LogisticsPathOptimizer:
def __init__(self, locations, constraints):
self.locations = locations
self.constraints = constraints
self.graph = self._build_graph()
def _build_graph(self):
G = nx.Graph()
for i, loc1 in enumerate(self.locations):
for j, loc2 in enumerate(self.locations):
if i != j:
distance = geodesic(loc1['coords'], loc2['coords']).km
time_estimate = distance / self.constraints['avg_speed']
cost = distance * self.constraints['cost_per_km']
G.add_edge(i, j, weight=cost, distance=distance, time=time_estimate)
return G
def find_optimal_route(self, start, end):
"""使用Dijkstra算法寻找最优路径"""
path = nx.shortest_path(self.graph, start, end, weight='weight')
total_cost = nx.shortest_path_length(self.graph, start, end, weight='weight')
total_distance = sum(self.graph[path[i]][path[i+1]]['distance']
for i in range(len(path)-1))
return path, total_cost, total_distance
# Streamlit应用界面
st.title("智能物流路径规划系统")
st.sidebar.header("约束条件设置")
avg_speed = st.sidebar.slider("平均速度(km/h)", 30, 120, 60)
cost_per_km = st.sidebar.number_input("每公里成本(元)", 1.0, 10.0, 2.5)
# 示例位置数据
locations = [
{'name': '仓库A', 'coords': (31.2304, 121.4737)},
{'name': '客户B', 'coords': (31.2204, 121.4837)},
{'name': '客户C', 'coords': (31.2404, 121.4637)}
]
optimizer = LogisticsPathOptimizer(locations, {
'avg_speed': avg_speed,
'cost_per_km': cost_per_km
})
start_point = st.selectbox("起点", [loc['name'] for loc in locations])
end_point = st.selectbox("终点", [loc['name'] for loc in locations])
if st.button("计算最优路径"):
start_idx = next(i for i, loc in enumerate(locations) if loc['name'] == start_point)
end_idx = next(i for i, loc in enumerate(locations) if loc['name'] == end_point)
path, cost, distance = optimizer.find_optimal_route(start_idx, end_idx)
st.success(f"最优路径成本: {cost:.2f}元")
st.info(f"总距离: {distance:.2f}公里")
st.write("路径顺序:", " → ".join([locations[i]['name'] for i in path]))
2. 实时供应链监控看板
功能特性：
- 实时货物追踪
- 库存水平监控
- 运输状态可视化
- 异常预警系统
import streamlit as st
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from datetime import datetime, timedelta
import random
class SupplyChainDashboard:
def __init__(self):
self.data = self._generate_sample_data()
def _generate_sample_data(self):
"""生成模拟供应链数据"""
dates = pd.date_range(start='2024-01-01', end='2024-01-31', freq='D')
products = ['电子产品', '食品', '服装', '药品', '机械设备']
warehouses = ['北京仓', '上海仓', '广州仓', '成都仓', '武汉仓']
data = []
for date in dates:
for product in products:
for warehouse in warehouses:
data.append({
'date': date,
'product': product,
'warehouse': warehouse,
'inventory': random.randint(100, 1000),
'orders': random.randint(10, 100),
'shipments': random.randint(5, 50)
})
return pd.DataFrame(data)
def create_dashboard(self):
st.title("实时供应链监控看板")
# 库存概览
col1, col2, col3 = st.columns(3)
total_inventory = self.data['inventory'].sum()
total_orders = self.data['orders'].sum()
total_shipments = self.data['shipments'].sum()
col1.metric("总库存", f"{total_inventory:,}")
col2.metric("总订单", f"{total_orders:,}")
col3.metric("总发货", f"{total_shipments:,}")
# 库存趋势图
inventory_trend = self.data.groupby('date')['inventory'].sum().reset_index()
fig1 = px.line(inventory_trend, x='date', y='inventory',
title='库存趋势变化')
st.plotly_chart(fig1)
# 仓库库存分布
warehouse_inventory = self.data.groupby('warehouse')['inventory'].sum().reset_index()
fig2 = px.pie(warehouse_inventory, values='inventory', names='warehouse',
title='各仓库库存分布')
st.plotly_chart(fig2)
# 产品销量热力图
product_sales = self.data.groupby(['product', 'warehouse'])['orders'].sum().reset_index()
fig3 = px.density_heatmap(product_sales, x='warehouse', y='product', z='orders',
title='产品-仓库销量热力图')
st.plotly_chart(fig3)
# 启动看板
dashboard = SupplyChainDashboard()
dashboard.create_dashboard()
3. 多目标优化算法
算法对比表：
| 算法类型 | 适用场景 | 时间复杂度 | 空间复杂度 | 优点 | 缺点 |
|---|---|---|---|---|---|
| Dijkstra | 单源最短路径 | O((V+E)logV) | O(V) | 保证最优解 | 不能处理负权边 |
| A*算法 | 启发式搜索 | O(b^d) | O(b^d) | 搜索效率高 | 需要好的启发函数 |
| 遗传算法 | 多目标优化 | O(n²) | O(n) | 全局搜索能力强 | 参数调优复杂 |
| 蚁群算法 | 路径优化 | O(n²·m) | O(n²) | 并行性好 | 收敛速度慢 |
import numpy as np
from typing import List, Tuple
import matplotlib.pyplot as plt
class MultiObjectiveLogisticsOptimizer:
def __init__(self, nodes: List[Tuple[float, float]],
demand: List[float], capacity: float):
self.nodes = nodes
self.demand = demand
self.capacity = capacity
self.n_nodes = len(nodes)
def _calculate_distance(self, i: int, j: int) -> float:
"""计算两点之间的欧几里得距离"""
return np.sqrt((self.nodes[i][0] - self.nodes[j][0])**2 +
(self.nodes[i][1] - self.nodes[j][1])**2)
def objective_functions(self, route: List[int]) -> Tuple[float, float, float]:
"""计算三个目标函数：总距离、总时间、总成本"""
total_distance = 0
total_time = 0
total_cost = 0
current_load = 0
for i in range(len(route) - 1):
from_node = route[i]
to_node = route[i + 1]
distance = self._calculate_distance(from_node, to_node)
time = distance / 60 # 假设平均速度60km/h
cost = distance * 2.5 # 假设每公里成本2.5元
total_distance += distance
total_time += time
total_cost += cost
# 考虑载重约束
current_load += self.demand[to_node]
if current_load > self.capacity:
# 违反载重约束，增加惩罚项
total_cost += 1000 * (current_load - self.capacity)
return total_distance, total_time, total_cost
def nsga_ii_optimization(self, population_size: int = 100,
generations: int = 100) -> List[List[int]]:
"""NSGA-II多目标优化算法实现"""
# 初始化种群
population = []
for _ in range(population_size):
route = list(range(self.n_nodes))
np.random.shuffle(route)
population.append(route)
# 多代进化
for generation in range(generations):
# 评估目标函数
objectives = [self.objective_functions(ind) for ind in population]
# 非支配排序和拥挤度计算
fronts = self._non_dominated_sorting(objectives)
crowding_distances = self._calculate_crowding_distance(objectives, fronts)
# 选择、交叉、变异
new_population = self._selection(population, fronts, crowding_distances)
population = new_population
return population
# 使用示例
nodes = [(0, 0), (10, 20), (30, 40), (50, 10), (70, 30)]
demand = [0, 100, 200, 150, 80] # 每个节点的需求
capacity = 300 # 车辆容量
optimizer = MultiObjectiveLogisticsOptimizer(nodes, demand, capacity)
optimal_routes = optimizer.nsga_ii_optimization()
print("找到的帕累托最优解:")
for i, route in enumerate(optimal_routes[:5]): # 显示前5个解
dist, time, cost = optimizer.objective_functions(route)
print(f"路线{i+1}: {route}, 距离: {dist:.2f}km, 时间: {time:.2f}h, 成本: {cost:.2f}元")
部署与集成
环境配置
# 克隆项目
git clone https://gitcode.com/GitHub_Trending/fr/fragments
cd fragments
# 安装依赖
npm install
# 配置环境变量
cat > .env.local << EOF
E2B_API_KEY="your-e2b-api-key"
OPENAI_API_KEY="your-openai-key"
NEXT_PUBLIC_SITE_URL="http://localhost:3000"
EOF
# 启动开发服务器
npm run dev
自定义物流模板
创建专门的物流优化模板：
{
"logistics-optimizer": {
"name": "物流路径优化专家",
"lib": [
"python",
"numpy",
"pandas",
"networkx",
"geopy",
"streamlit",
"plotly"
],
"file": "logistics_app.py",
"instructions": "专门用于物流路径优化和供应链管理的应用。集成多种优化算法，支持实时数据可视化和多目标决策。",
"port": 8501
}
}
性能优化策略
1. 算法优化
- 并行计算：利用多核处理器并行执行路径计算
- 缓存机制：缓存常用路径计算结果，减少重复计算
- 近似算法：对于大规模问题，使用近似算法保证实时性
2. 数据管理
- 增量更新：只处理变化的数据，减少计算量
- 数据分区：按地理区域或业务维度分区处理
- 压缩存储：使用高效的数据压缩格式
3. 系统架构
flowchart TD
A[用户请求] --> B[API网关]
B --> C[负载均衡]
C --> D[计算节点1]
C --> E[计算节点2]
C --> F[计算节点N]
D --> G[Redis缓存]
E --> G
F --> G
G --> H[数据库集群]
subgraph 监控系统
I[性能监控]
J[错误日志]
K[业务指标]
end
D --> I
E --> I
F --> I创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考
