人工智能在物流行业的应用

import googlemaps
from sklearn.cluster import KMeans
import numpy as np

# 初始化Google Maps API
gmaps = googlemaps.Client(key='YOUR_GOOGLE_MAPS_API_KEY')

# 示例配送地点数据
locations = [
    {'lat': 37.7749, 'lng': -122.4194},  # San Francisco
    {'lat': 34.0522, 'lng': -118.2437},  # Los Angeles
    {'lat': 36.1699, 'lng': -115.1398},  # Las Vegas
    {'lat': 40.7128, 'lng': -74.0060},   # New York
    # 添加更多地点
]

# 提取坐标
coords = np.array([[loc['lat'], loc['lng']] for loc in locations])

# 使用KMeans聚类划分配送中心
kmeans = KMeans(n_clusters=2)
kmeans.fit(coords)
centroids = kmeans.cluster_centers_
labels = kmeans.labels_

# 输出配送中心
print("配送中心坐标:", centroids)

# 获取并优化路线
def get_route(start, end):
    directions = gmaps.directions(start, end, mode="driving")
    return directions

# 示例：获取从San Francisco到Los Angeles的路线
route = get_route("San Francisco", "Los Angeles")
print(route)

import pandas as pd
from fbprophet import Prophet
import matplotlib.pyplot as plt

# 生成示例订单数据
date_rng = pd.date_range(start='2023-01-01', end='2023-12-31', freq='D')
order_data = pd.DataFrame(date_rng, columns=['ds'])
order_data['y'] = np.random.poisson(lam=50, size=len(order_data))

# 初始化并拟合Prophet模型
model = Prophet()
model.fit(order_data)

# 进行预测
future = model.make_future_dataframe(periods=30)
forecast = model.predict(future)

# 可视化预测结果
fig = model.plot(forecast)
plt.title('物流需求预测')
plt.xlabel('日期')
plt.ylabel('订单数量')
plt.show()