华为2022年软挑赛初赛试题及初级代码（成渝赛区）
目录
1.赛题描述
2.全部代码
1.赛题描述
2.全部代码
# import psutil ##查看程序所占用的内存空间
# import os
import numpy as np
import configparser
import time
start =time.clock()
config_path = 'F:\\桌面\\data\\config.ini'
path1 = "F:\\桌面\\data\\demand.csv"
path2 = "F:\\桌面\\data\\site_bandwidth.csv"
path3 = "F:\\桌面\\data\\qos.csv"
path4 = "F:\\桌面\\data\\output\\solution.txt"
##路径
# config_path = '/data/config.ini'
# path1 = "/data/demand.csv"
# path2 = "/data/site_bandwidth.csv"
# path3 = "/data/qos.csv"
# path4 = "/output/solution.txt"
#读取配置文件
conf = configparser.ConfigParser()
conf.read(config_path, encoding="utf-8")
qos_constraint = conf.getint("config", "qos_constraint")
##获取时间度个数
data1 = np.loadtxt(path1, dtype=str, delimiter=',', skiprows=1)
t = len(data1)
# print(t)
##获取客户结点ID
data0 = np.loadtxt(path1, dtype=str, delimiter=',')
data2 = data0[0].tolist()
data2.pop(0)
n = data2
##获取边缘节点ID
data3 = np.loadtxt(path2, dtype=str, delimiter=',', skiprows=1)
m = []
for item3 in data3:
m.append(item3[0].tolist()[0:2])
# print(data3)
# print(m)
##定义客户节点类
class custom:
def __init__(self, name):
self.name = name
##定义边缘节点类
class bianyuan(custom):
# cj = 430080
def __init__(self, name, data, cj):
super(bianyuan, self).__init__(name)
self.cj = cj
self.data = data
def distribute(self, slip): ###遍历每个客户结点更新
self.data = self.data + slip
### 读取一个时间刻度的需求量数据
def read_demand_data(path, time_chip):
data1 = np.loadtxt(path, dtype=str, delimiter=',', skiprows=1)
m_demand = {}
for item in range(1, len(n)+1):
item_data = data1[time_chip-1][item]
m_demand.update({n[item-1]: item_data})
# print(m_demand)
return m_demand
# read_demand_data("E:\\桌面\\华为云作业\\线下调试数据\\data\\demand.csv")
# 读取一个时间刻度的qos限制数据
def read_qos_data(path, i):
data2 = np.loadtxt(path, dtype=str, delimiter=',', skiprows=1)
# print(data2)
m_qos = {}
for item in data2:
m_qos.update({"%s"%(item[0]):int(item[i])})
# print(m_qos)
return m_qos
# for i in range(1, len(n)+1): ##表示的是客户节点的循环，应该放在程序的开始
# read_qos_data("E:\\桌面\\华为云作业\\线下调试数据\\data\\qos.csv", i)
# 读取一个时间刻度的单个边缘节点限制数据
def read_bianyuan_limit_data(path):
data3= np.loadtxt(path, dtype=str, delimiter=',', skiprows=1)
m_limit = {}
for item in data3:
m_limit.update({"%s"%(item[0]):int(item[1])})
# locals()['bianyuan_limit_'+item[0]+'_data'] = m_limit.get("%s"%item[0])
# print(locals()['bianyuan_limit_'+name+'_data'])
return m_limit
# read_bianyuan_limit_data("E:\\桌面\\华为云作业\\线下调试数据\\data\\site_bandwidth.csv", "B")
if __name__ == "__main__":
# time_chip = 1
wj = 0 ##用来存方案整体成本
for item7 in m:
locals()["bianyuan_list_" + str(item7)] = [] ##创建以边缘节点命名的列表m个
list_all = {} ###用于存单个边缘节点所有时刻的带宽值
for time_chip in range(1, t+1):
for i in range(1, len(n) + 1): ##表示的是客户节点的循环，应该放在程序的开始
m_demand = read_demand_data(path1, time_chip) # 读取需求，返回在一个时间度下每个客户节点需求
m_qos = read_qos_data(path3, i) # 读取qos，返回一个时间度下，每个客户节点与每个边缘节点的qos
m_limit = read_bianyuan_limit_data(path2) ##读取限制性数据，
# 返回每个边缘节点限制
m_qos_suit = {k:v for k, v in m_qos.items() if v<=qos_constraint} ##判断符合条件的qos
m_qos_nosuit = {k:v for k, v in m_qos.items() if v>qos_constraint}
m_qos_suit_num = len(m_qos_suit)
# print("合适结点数目:", m_qos_suit_num)
m_qos_nosuit_num = len(m_qos_nosuit)
# print(m_qos_suit)
# print(m_qos_nosuit)
Dj = m_demand.get("%s"%n[i-1]) ##获取每个节点的需求数据
slip = int(Dj)//m_qos_suit_num + 1 ##平均分配数量
# print(slip)
m_qos_suit_keys = m_qos_suit.keys() ##获取适合的键（边缘节点名字）
m_qos_nosuit_keys = m_qos_nosuit.keys() ##获取不适合的键
# print(m_qos_nosuit_keys)
# print(m_qos_suit_keys)
# print(locals()["custom_suit_" + str(n[i-1])].name_list)
if i == 1: ##只在第一轮客户结点时,创建所有边缘结点对象
# flag1 = 1 ##设置flag，用于批量命名合适结点和不合适结点的对象
for name in m_qos_suit_keys:
# for i in range(1,101):
# if flag1<(m_qos_suit_num+1):
locals()["bianyuan_suit_"+name] = bianyuan(name, 0, m_limit.get(name)) ##批量命名满足要求结点对象
locals()["bianyuan_suit_"+name].distribute(slip) ##更新其内部数据
# flag1 += 1
# print(locals()['bianyuan_suit_A'].data)
# flag2 = 1
for name1 in m_qos_nosuit_keys:
# for i in range(1,101):
# if flag2<(m_qos_nosuit_num+1):
locals()["bianyuan_suit_"+name1] = bianyuan(name1, 0, m_limit.get(name1)) ##批量命名不满足要求结点对象
locals()["bianyuan_suit_"+name1].distribute(0)
# flag2 += 1
else:
for item5 in m_qos_suit_keys:
locals()["bianyuan_suit_"+item5].distribute(slip)
for item6 in m_qos_nosuit_keys:
locals()["bianyuan_suit_"+item6].distribute(0)
##打印结果
str_content1 = "%s:" % n[i-1]
str_content = "%s:" % n[i-1]
# print(bianyuan_name)
for item2 in m_qos_suit_keys:
str_content1 += "<%s,%d>," % (item2, slip)
with open(path4, 'a') as file_handle:
if m_demand.get(n[i-1]) and i == 1 and time_chip == 1:
file_handle.write(str_content1) # 写入
elif m_demand.get(n[i-1]):
file_handle.write('\n') # 有时放在循环里面需要自动转行，不然会覆盖上一条数据
file_handle.write(str_content1) # 写入
elif m_demand.get(n[i-1]) != true and i == 1 and time_chip == 1:
file_handle.write(str_content) # 写入
elif m_demand.get(n[i-1]) != true:
file_handle.write('\n') # 有时放在循环里面需要自动转行，不然会覆盖上一条数据
file_handle.write(str_content) # 写入
# print(str_content)
##存一个时刻下边缘节点的的流量
for item7 in m: ###将一个时刻的所有边缘节点带宽存到字典里边
list_all.update({item7:(locals()["bianyuan_suit_"+item7].data)})
locals()["bianyuan_list_" + item7].append(list_all.get(item7))
# print(list_all)
# time_chip += 1
##计算成本
for item8 in m:
locals()["bianyuan_list_" + str(item8)].sort()
###获得每个边缘节点所有时刻的95带宽值
print(locals()["bianyuan_list_" + str(item8)])
wjj = locals()["bianyuan_list_" + str(item8)][int(np.ceil((len(locals()["bianyuan_list_" + str(item8)]) * 0.95))) - 1]
print(wjj)
wj += locals()["bianyuan_list_" + str(item8)][int(np.ceil((len(locals()["bianyuan_list_" + str(item8)]) * 0.95))) - 1]
# wj = locals()["bianyuan_suit_"+str(item4)].return_wj()
print("总成本为：", wj)
# print(u'当前进程的内存使用：%.4f GB' % (psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024 / 1024))
end = time.clock()
print('Running time: %s Seconds' % (end - start))
# main(path1, path2, path3, path4)