使用python编写一份模拟退火算法来优化公交车当天的发车时刻的代码，要求考虑每日不同的时间段以及客流量的变化，同时把我需要输入的数据在代码旁边注释清楚，结果要求可以输出客流量变化图标和当天的发车时刻表，时刻表要细化到分钟

以下是一份简单的模拟退火算法来优化公交车发车时刻的 Python 代码，该代码考虑了每日不同的时间段以及客流量的变化。

import random
import math
import matplotlib.pyplot as plt

# 客流量数据，每小时的客流量，共24小时
passenger_data = [10, 20, 30, 50, 60, 80, 100, 120, 150, 180, 200, 180, 150, 120, 100, 80, 60, 50, 30, 20, 10, 5, 5, 5]

# 时间段数据，每个时间段的起始时间和结束时间，以及每个时间段的发车间隔
time_data = [
    {"start": "06:00", "end": "08:00", "interval": 5},
    {"start": "08:00", "end": "10:00", "interval": 6},
    {"start": "10:00", "end": "17:00", "interval": 10},
    {"start": "17:00", "end": "20:00", "interval": 7},
    {"start": "20:00", "end": "22:00", "interval": 8},
    {"start": "22:00", "end": "24:00", "interval": 10},
]

# 计算总客流量
total_passenger = sum(passenger_data)

# 计算每分钟平均客流量
avg_passenger_per_minute = total_passenger / (24 * 60)

# 生成初始解
initial_solution = []
for time_slot in time_data:
    start = int(time_slot["start"].split(":")[0]) * 60 + int(time_slot["start"].split(":")[1])
    end = int(time_slot["end"].split(":")[0]) * 60 + int(time_slot["end"].split(":")[1])
    interval = time_slot["interval"]
    num_buses = math.ceil((end - start) / interval)
    for i in range(num_buses):
        # 随机生成发车时间
        initial_solution.append(random.randint(start, end))

# 计算初始解的客流量
def calculate_passenger(solution):
    passenger_per_slot = [0] * len(time_data)
    for i in range(len(time_data)):
        time_slot = time_data[i]
        start = int(time_slot["start"].split(":")[0]) * 60 + int(time_slot["start"].split(":")[1])
        end = int(time_slot["end"].split(":")[0]) * 60 + int(time_slot["end"].split(":")[1])
        interval = time_slot["interval"]
        for j in range(len(solution)):
            if solution[j] >= start and solution[j] < end:
                passenger_per_slot[i] += passenger_data[solution[j] // 60]
        passenger_per_slot[i] /= interval
    return sum(passenger_per_slot)

# 计算温度
def temperature(current_temperature, iteration):
    return current_temperature * 0.95 ** iteration

# 计算新解
def get_new_solution(current_solution, current_temperature):
    new_solution = current_solution.copy()
    index = random.randint(0, len(new_solution) - 1)
    new_solution[index] = random.randint(0, 24 * 60 - 1)
    delta = calculate_passenger(new_solution) - calculate_passenger(current_solution)
    if delta > 0:
        return new_solution
    else:
        if math.exp(delta / current_temperature) > random.random():
            return new_solution
        else:
            return current_solution

# 模拟退火算法
def simulated_annealing(initial_solution, initial_temperature):
    current_solution = initial_solution
    current_temperature = initial_temperature
    best_solution = current_solution
    best_score = calculate_passenger(current_solution)
    iteration = 0
    while current_temperature > 0.01:
        new_solution = get_new_solution(current_solution, current_temperature)
        new_score = calculate_passenger(new_solution)
        if new_score > best_score:
            best_solution = new_solution
            best_score = new_score
        current_solution = new_solution
        current_temperature = temperature(current_temperature, iteration)
        iteration += 1
    return best_solution

# 优化求解
best_solution = simulated_annealing(initial_solution, 1000)

# 输出时刻表
time_table = [0] * (24 * 60)
for i in range(len(best_solution)):
    time_table[best_solution[i]] += 1
for i in range(len(time_table)):
    if time_table[i] > 0:
        hour = i // 60
        minute = i % 60
        print("{:02d}:{:02d} - {:02d}:{:02d}".format(hour, minute, hour, minute + 5 * time_table[i]))

# 输出客流量变化图表
passenger_per_minute = [0] * (24 * 60)
for i in range(len(passenger_data)):
    for j in range(60):
        passenger_per_minute[i * 60 + j] = passenger_data[i] / 60
plt.plot(passenger_per_minute)
plt.show()

在上述代码中，`passenger_data` 表示每小时的客流量，共24小时。`time_data` 表示每个时间段的起始时间和结束时间，以及每个时间段的发车间隔。代码中使用模拟退火算法来优化公交车的发车时刻表，以最大化客流量。同时，代码还输出了客流量变化图表和当天的发车时刻表，时刻表细化到分钟。