Python 智能停车场运营分析系统：数据可视化与分析

以下示例代码使用 Python 实现一个简单的智能停车场运营分析系统，并通过图表展示关键运营指标，帮助您更直观地了解停车场状况。

import numpy as np
import matplotlib.pyplot as plt

# 停车时间的分布情况
def parking_time_distribution(parking_times):
    plt.hist(parking_times, bins=10)
    plt.xlabel('Parking Time (minutes)')
    plt.ylabel('Frequency')
    plt.title('Parking Time Distribution')
    plt.show()

# 停车高峰的时间统计
def peak_time_statistics(entrance_times):
    hourly_counts = np.zeros(24)
    for time in entrance_times:
        hour = int(time.split(':')[0])
        hourly_counts[hour] += 1
    
    plt.bar(range(24), hourly_counts)
    plt.xlabel('Hour')
    plt.ylabel('Number of Cars')
    plt.title('Peak Time Statistics')
    plt.show()

# 每周繁忙的比例
def weekly_busyness_ratio(entrance_times):
    weekdays = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
    daily_counts = np.zeros(7)
    for time in entrance_times:
        day = time.split(',')[0]
        daily_counts[weekdays.index(day)] += 1
    
    plt.pie(daily_counts, labels=weekdays, autopct='%1.1f%%')
    plt.title('Weekly Busyness Ratio')
    plt.show()

# 月收入分析
def monthly_revenue_analysis(revenue_data):
    months = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']
    monthly_revenue = np.zeros(12)
    for data in revenue_data:
        month = data[0].split('-')[1]
        monthly_revenue[months.index(month)] += data[1]
    
    plt.plot(range(1, 13), monthly_revenue)
    plt.xlabel('Month')
    plt.ylabel('Revenue')
    plt.title('Monthly Revenue Analysis')
    plt.show()

# 每日接待车辆的统计
def daily_car_count(entrance_times):
    daily_counts = np.zeros(31)
    for time in entrance_times:
        day = int(time.split(',')[1].split('-')[0])
        daily_counts[day-1] += 1
    
    plt.bar(range(1, 32), daily_counts)
    plt.xlabel('Day')
    plt.ylabel('Number of Cars')
    plt.title('Daily Car Count')
    plt.show()

# 示例数据
parking_times = np.random.randint(30, 180, 1000)
entrance_times = ['Monday, 01-01-2022 08:30', 'Monday, 01-01-2022 12:15', 'Tuesday, 01-02-2022 09:45', 'Wednesday, 01-03-2022 17:30', 'Thursday, 01-04-2022 10:00']
revenue_data = [('2022-01-01', 500), ('2022-02-01', 800), ('2022-02-15', 1000), ('2022-03-01', 1200), ('2022-05-01', 900)]

# 调用函数进行分析
parking_time_distribution(parking_times)
peak_time_statistics(entrance_times)
weekly_busyness_ratio(entrance_times)
monthly_revenue_analysis(revenue_data)
daily_car_count(entrance_times)

请注意，这只是一个简单的示例代码，实际情况中可能需要根据具体需求进行更多的数据处理和分析。