用python写一段程序，需求1：读取excel中的x与y轴坐标值以及对应的高度。需求2：赋予每个坐标点3种状态，初始状态为0 需求3：读取excel表格中的风速风向数据，根据给定的计算公式计算蔓延因子 需求4：给定初始起火点，即状态从0到1，给定初始速度，乘以蔓延因子得到八个方向上的蔓延速度，乘以时间步长5分钟，得到新的蔓延范围 结束计算时间为1800分钟

以下是基于Pandas库和xlrd库的程序实现，读取excel中的x与y轴坐标值以及对应的高度，并赋予每个坐标点3种状态，初始状态为0。

import pandas as pd
import xlrd

# 读取Excel中的x与y轴坐标值以及对应的高度
file_path = 'data.xlsx'
data = xlrd.open_workbook(file_path)
sheet = data.sheet_by_index(0)
x = sheet.col_values(0)[1:]
y = sheet.col_values(1)[1:]
h = sheet.col_values(2)[1:]

# 赋予每个坐标点3种状态，初始状态为0
df = pd.DataFrame({'x': x, 'y': y, 'h': h})
df['state1'] = 0
df['state2'] = 0
df['state3'] = 0
print(df.head())

下面是计算蔓延因子的程序实现，读取excel表格中的风速风向数据，根据给定的计算公式计算蔓延因子。

# 读取Excel表格中的风速风向数据
wind_data = pd.read_excel(file_path, sheet_name='wind', header=0)

# 根据给定的计算公式计算蔓延因子
k1 = 0.864
k2 = 0.092
k3 = 0.044
k4 = 0.0
k5 = 0.0
k6 = 0.0
k7 = 0.0
k8 = 0.0
alpha = 0.01
beta = 0.1
gamma = 0.7

for i in range(len(wind_data)):
    wind_speed = wind_data.loc[i, 'wind_speed']
    wind_direction = wind_data.loc[i, 'wind_direction']
    if wind_speed >= 0.5:
        k4 = k1 * (wind_speed ** alpha) * ((1 - beta) ** wind_speed) * ((1 - gamma) ** (wind_speed ** 2))
        k5 = k1 * (wind_speed ** alpha) * ((1 - beta) ** (wind_speed ** 2)) * ((1 - gamma) ** wind_speed)
    k6 = k2 * (wind_speed ** alpha)
    k7 = k3 * (wind_speed ** alpha)
    k8 = k1 * (wind_speed ** alpha) * ((1 - beta) ** (wind_speed ** 2)) * ((1 - gamma) ** (wind_speed ** 2))
print('k1:', k1)
print('k2:', k2)
print('k3:', k3)
print('k4:', k4)
print('k5:', k5)
print('k6:', k6)
print('k7:', k7)
print('k8:', k8)

最后，给定初始起火点，即状态从0到1，给定初始速度，乘以蔓延因子得到八个方向上的蔓延速度，乘以时间步长5分钟，得到新的蔓延范围并结束计算时间为1800分钟的程序实现。

import math

# 给定初始起火点，即状态从0到1
df.loc[df.index[(df['x'] == 20) & (df['y'] == 20)], 'state1'] = 1

# 给定初始速度
v = 20

# 乘以蔓延因子得到八个方向上的蔓延速度，乘以时间步长5分钟，得到新的蔓延范围
for t in range(1, 181):
    for i in range(len(df)):
        if df.loc[i, 'state1'] == 1:
            x0 = df.loc[i, 'x']
            y0 = df.loc[i, 'y']
            h0 = df.loc[i, 'h']
            if math.isnan(h0):
                h0 = 0
            for j in range(len(df)):
                if i != j:
                    x1 = df.loc[j, 'x']
                    y1 = df.loc[j, 'y']
                    h1 = df.loc[j, 'h']
                    if math.isnan(h1):
                        h1 = 0
                    dx = x1 - x0
                    dy = y1 - y0
                    distance = math.sqrt(dx ** 2 + dy ** 2)
                    if distance <= 1000:
                        cosine = dx / distance
                        sine = dy / distance
                        direction = math.degrees(math.acos(cosine))
                        if sine < 0:
                            direction = 360 - direction
                        if direction >= 337.5 or direction < 22.5:
                            k = k1
                        elif direction >= 22.5 and direction < 67.5:
                            k = k2
                        elif direction >= 67.5 and direction < 112.5:
                            k = k3
                        elif direction >= 112.5 and direction < 157.5:
                            k = k4
                        elif direction >= 157.5 and direction < 202.5:
                            k = k5
                        elif direction >= 202.5 and direction < 247.5:
                            k = k6
                        elif direction >= 247.5 and direction < 292.5:
                            k = k7
                        else:
                            k = k8
                        v1 = v * k * ((h0 + h1) / 2) ** (-1 / 4)
                        dt = 5
                        dx1 = v1 * cosine * dt
                        dy1 = v1 * sine * dt
                        x2 = x0 + dx1
                        y2 = y0 + dy1
                        if x2 >= 0 and x2 <= 100 and y2 >= 0 and y2 <= 100:
                            df.loc[df.index[(df['x'] == x2) & (df['y'] == y2)], 'state2'] = 1
    df['state1'] = df['state2']
    df['state2'] = 0
    df['state3'] = 0
    print('t:', t)
    print(df[df['state1'] == 1])
    if df[df['state1'] == 1].empty:
        break

程序执行完毕后，会输出每个时间步长上的蔓延范围。在这个实现中，由于我没有提供完整的数据，所以蔓延因子的计算可能有误，请根据具体情况自行调整。