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easy_university_selection.py
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# -*- coding: utf-8 -*-
"""easy university section"""
__author__ = 'Andy Siu'
# ////////////////////////////////////////////////////////////////////
# // _ooOoo_ //
# // o8888888o //
# // 88" . "88 //
# // (| ^_^ |) //
# // O\ = /O //
# // ____/`---'\____ //
# // .' \\| |// `. //
# // / \\||| : |||// \ //
# // / _||||| -:- |||||- \ //
# // | | \\\ - /// | | //
# // | \_| ''\---/'' | | //
# // \ .-\__ `-` ___/-. / //
# // ___`. .' /--.--\ `. . ___ //
# // ."" '< `.___\_<|>_/___.' >'"". //
# // | | : `- \`.;`\ _ /`;.`/ - ` : | | //
# // \ \ `-. \_ __\ /__ _/ .-` / / //
# // ========`-.____`-.___\_____/___.-`____.-'======== //
# // `=---=' //
# // ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ //
# // 佛祖保佑 永无BUG 永不修改 //
# ////////////////////////////////////////////////////////////////////
import sys
import os
import urllib2
import xml.sax
from xml.dom.minidom import parse
import xml.dom.minidom
from openpyxl import Workbook
import pickle
import time
# 分数划线
class ScoreLine:
def __init__(self):
pass
year = ''
region = ''
subject = ''
tier = ''
score = 0
# 省入取分数
class ProvinceScore:
def __init__(self):
pass
year = 0
region = ''
school = ''
subject = ''
maxScore = 0
avgScore = 0
minScore = 0
tier = ''
hope = 0
hot = 0
# 专业分数线
class MajorScore:
def __init__(self):
pass
year = 0
region = ''
school = ''
subject = ''
maxScore = 0
avgScore = 0
minScore = 0
tier = ''
hope = 0
hot = 0
major = ''
majorName = ''
# 高校信息
class UniversityInfo:
def __init__(self):
pass
longitude = ''
latitude = ''
name = ''
region = ''
regionCode = ''
level = ''
hot = 0
classes = ''
classRank = 0
web = ''
code = ''
# 载入所有年份的分数线
def load_score_line():
files = os.listdir('./resource/score_line/' + regionCode)
sls = {} # year:region:subject:tier score
for sFile in files:
f = open('./resource/score_line/' + regionCode + '/' + sFile)
iter_f = iter(f) # 创建迭代器
for line in iter_f:
line = ''.join(line.split())
arr = line.split(",")
if len(arr) < 4: continue
sl = ScoreLine()
sl.year = arr[0]
sl.region = regionCodeDict[arr[1]]
if arr[2] == '理科':
sl.subject = '10035'
elif arr[2] == '文科':
sl.subject = '10034'
else:
continue
if '一' in arr[3]:
sl.tier = '10036'
elif '二' in arr[3]:
sl.tier = '10037'
elif '三' in arr[3]:
sl.tier = '10038'
elif '专科' or '高职' in arr[3]:
sl.tier = '10148'
else:
continue
# 专为福建没有三本特殊处理
if regionCode == '10024':
if sl.tier == '10148':
sl.tier = '10038'
sl.score = int(arr[4])
sls[sl.year + ',' + sl.region + ',' + sl.subject + ',' + sl.tier] = sl
return sls
# 加载高校在各省的专业录取分数线
def load_major_score():
score_path = './resource/spider_files/major_score_line/' + regionCode + '_' + subject + '.dump'
if os.path.exists(score_path):
tmp_file = open(score_path, 'rb')
d = pickle.load(tmp_file)
tmp_file.close()
print '共加载' + str(len(d)) + '条专业分数线数据'
return d
paths = os.listdir('./resource/spider_files/major_score_line/' + regionCode + '/')
pss = {}
for path in paths:
files = os.listdir('./resource/spider_files/major_score_line/' + regionCode + '/' + path)
for sFile in files:
if not os.path.isdir(sFile):
if not (subject in sFile): continue
print sFile
dom = xml.dom.minidom.parse(
'./resource/spider_files/major_score_line/' + regionCode + '/' + path + '/' + sFile)
root = dom.documentElement
major_elements = root.getElementsByTagName("areapiont")
for major_element in major_elements:
year_node = major_element.getElementsByTagName("year")[0]
y = ''
if len(year_node.childNodes) > 0: y = year_node.childNodes[0].nodeValue
max_score_node = major_element.getElementsByTagName("maxfs")[0]
max_score = ''
if len(max_score_node.childNodes) > 0: max_score = max_score_node.childNodes[0].nodeValue
min_score_node = major_element.getElementsByTagName("minfs")[0]
min_score = ''
if len(min_score_node.childNodes) > 0: min_score = min_score_node.childNodes[0].nodeValue
avg_score_node = major_element.getElementsByTagName("varfs")[0]
avg_score = ''
if len(avg_score_node.childNodes) > 0: avg_score = avg_score_node.childNodes[0].nodeValue
tier_node = major_element.getElementsByTagName("pc")[0]
tier = ''
if len(tier_node.childNodes) > 0: tier = tier_node.childNodes[0].nodeValue
major_name_node = major_element.getElementsByTagName("specialname")[0]
major_name = ''
if len(major_name_node.childNodes) > 0: major_name = major_name_node.childNodes[0].nodeValue
ms = MajorScore()
if not ('--' == y or '' == y): ms.year = int(y)
if ms.year < int(year) - 3: continue
if not ('--' == max_score or '' == max_score): ms.maxScore = int(max_score[0:3])
if not ('--' == min_score or '' == min_score): ms.minScore = int(min_score[0:3])
if not ('--' == avg_score or '' == avg_score): ms.avgScore = int(avg_score[0:3])
tier = tier.encode('utf-8')
if '一' in tier:
tier_code = '10036'
elif '二' in tier:
tier_code = '10037'
elif '三' in tier:
tier_code = '10038'
elif '专' in tier:
tier_code = '10148'
elif '提前' in tier:
tier_code = '10149'
else:
continue
major_name = major_name.encode('utf-8')
ms.tier = tier_code
ms.region = regionCode
ms.school = path
ms.subject = subject
ms.majorName = major_name
if majorCodeDict.has_key(major_name):
ms.major = majorCodeDict[major_name]
# 学校 年份 福建 文科 批次 = 清华大学2016年在福建地区文科第一批次招生分数线
key = path + ',' + y + ',' + regionCode + ',' + subject + ',' + tier_code + ',' + ms.major
pss[key] = ms
with open(score_path, 'wb') as pickle_file:
pickle.dump(pss, pickle_file)
pickle_file.close()
return pss
# 加载高校在各省的录取分数线
def load_province_score():
score_path = './resource/spider_files/province_score_line/' + regionCode + '_' + subject + '.dump'
if os.path.exists(score_path):
tmp_file = open(score_path, 'rb')
d = pickle.load(tmp_file)
tmp_file.close()
print '共加载' + str(len(d)) + '条省录取分数线数据'
return d
paths = os.listdir('./resource/spider_files/province_score_line/' + regionCode + '/')
pss = {}
for path in paths:
files = os.listdir('./resource/spider_files/province_score_line/' + regionCode + '/' + path)
for sFile in files:
if not os.path.isdir(sFile):
if not (subject in sFile): continue
print sFile
dom = xml.dom.minidom.parse(
'./resource/spider_files/province_score_line/' + regionCode + '/' + path + '/' + sFile)
root = dom.documentElement
score_elements = root.getElementsByTagName("score")
for score_element in score_elements:
# print score.nodeName
year_node = score_element.getElementsByTagName("year")[0]
y = ''
if len(year_node.childNodes) > 0: y = year_node.childNodes[0].nodeValue
# print (yearNode.childNodes)
max_score_node = score_element.getElementsByTagName("maxScore")[0]
max_score = ''
if len(max_score_node.childNodes) > 0: max_score = max_score_node.childNodes[0].nodeValue
min_score_node = score_element.getElementsByTagName("minScore")[0]
min_score = ''
if len(min_score_node.childNodes) > 0: min_score = min_score_node.childNodes[0].nodeValue
avg_score_node = score_element.getElementsByTagName("avgScore")[0]
avg_score = ''
if len(avg_score_node.childNodes) > 0: avg_score = avg_score_node.childNodes[0].nodeValue
tier_node = score_element.getElementsByTagName("rb")[0]
tier = ''
if len(tier_node.childNodes) > 0: tier = tier_node.childNodes[0].nodeValue
ps = ProvinceScore()
if not ('--' == y or '' == y): ps.year = int(y)
if ps.year < int(year) - 3: continue
if not ('--' == max_score or '' == max_score): ps.maxScore = int(max_score[0:3])
if not ('--' == min_score or '' == min_score): ps.minScore = int(min_score[0:3])
if not ('--' == avg_score or '' == avg_score): ps.avgScore = int(avg_score[0:3])
tier = tier.encode('utf-8')
if '一' in tier:
tier_code = '10036'
elif '二' in tier:
tier_code = '10037'
elif '三' in tier:
tier_code = '10038'
elif '专' in tier:
tier_code = '10148'
elif '提前' in tier:
tier_code = '10149'
else:
continue
ps.tier = tier_code
ps.region = regionCode
ps.school = path
ps.subject = subject
# 学校 年份 福建 文科 批次 = 清华大学2016年在福建地区文科第一批次招生分数线
key = path + ',' + y + ',' + regionCode + ',' + subject + ',' + tier_code
pss[key] = ps
with open(score_path, 'wb') as pickle_file:
pickle.dump(pss, pickle_file)
pickle_file.close()
return pss
def load_university_info():
dump_file = './resource/university_info.dump'
university_dict = {}
if os.path.exists(dump_file):
f = open(dump_file, 'rb')
d = pickle.load(f)
f.close()
return d
f = open('./resource/university_info.csv')
iter_f = iter(f) # 创建迭代器
for line in iter_f:
if line.startswith('#'): continue
line = ''.join(line.split())
university = UniversityInfo()
arr = line.split(',')
university.longitude = arr[0]
university.latitude = arr[1]
university.name = arr[2]
university.region = arr[3]
university.regionCode = regionCodeDict[arr[3]]
university.level = arr[4]
university.hot = arr[5]
university.classes = arr[6]
university.classRank = arr[7]
university.web = arr[8]
university.code = arr[9]
university_dict[arr[9]] = university
with open(dump_file, 'wb') as pickle_file:
pickle.dump(university_dict, pickle_file)
pickle_file.close()
return university_dict
def init_cvs_kv(path, reverse):
kv = {}
f = open(path)
iter_f = iter(f) # 创建迭代器
for line in iter_f:
line = ''.join(line.split())
arr = line.split(',')
if not len(arr) == 2: continue
if reverse:
kv[arr[1]] = arr[0]
else:
kv[arr[0]] = arr[1]
return kv
def init_spider(path):
url_set = set()
if os.path.exists(path):
f = open(path)
iter_f = iter(f) # 创建迭代器
for line in iter_f:
line = ''.join(line.split())
url_set.add(line)
return url_set
# 抓取大学专业分
def spider_university_major_score_line(info):
spider_score_line('./resource/spider_files/major_score_line/',
'http://gkcx.eol.cn/commonXML/schoolSpecialPoint/schoolSpecialPoint', 'schoolSpecialPoint', '',
info)
# 抓取大学省录取分
def spider_university_province_score_line(tier, info):
spider_score_line('./resource/spider_files/province_score_line/',
'http://gkcx.eol.cn/schoolhtm/scores/provinceScores', 'provinceScores', tier, info)
def spider_score_line(save_path, spider_url, xml_name, tier, info):
count = 0
url404 = init_spider(save_path + regionCode + '_404.url')
has_spider = init_spider(save_path + regionCode + '_spider.url')
url404_size = len(url404)
has_spider_size = len(has_spider)
if '' == tier:
file_suffix = '.xml'
else:
file_suffix = '_' + tier + '.xml'
url_base = spider_url + '[university_code]_' + regionCode + '_' + subject + file_suffix
for k in universityInfoDict:
url = url_base.replace('[university_code]', universityInfoDict[k].code)
if url in url404: continue
count = count + 1
if url in has_spider: continue
print url
req = urllib2.Request(url)
res_data = urllib2.urlopen(req)
if "http://gkcx.eol.cn/404.htm" == res_data.url:
url404.add(url)
continue
has_spider.add(url)
res = res_data.read()
path = save_path + regionCode + '/' + universityInfoDict[k].code
if not os.path.exists(path): os.makedirs(path)
xml_file = open(
path + '/' + xml_name + universityInfoDict[
k].code + '_' + regionCode + '_' + subject + file_suffix, 'w')
xml_file.write(res)
xml_file.close()
if not len(url404) == url404_size:
wr = ''
for U in url404:
wr = wr + U + '\n'
tmp_file = open(save_path + regionCode + '_404.url', 'w')
tmp_file.write(str(wr)) # 写入内容，如果没有该文件就自动创建
tmp_file.close() # (关闭文件)
if not len(has_spider) == has_spider_size:
wr = ''
for U in has_spider:
wr = wr + U + '\n'
tmp_file = open(save_path + regionCode + '_spider.url', 'w')
tmp_file.write(str(wr)) # 写入内容，如果没有该文件就自动创建
tmp_file.close() # (关闭文件)
print info + '抓取完成,共抓取' + str(count) + '个文件'
# 筛选高校
def filter_university(scores):
year_int = int(year)
last1 = evaluate_score[year_int - 1]
last2 = evaluate_score[year_int - 2]
last3 = evaluate_score[year_int - 3]
result = []
school_set = set()
for k in scores:
s = scores[k]
is_filter = True
if s.school in school_set: continue
if not subject == s.subject: continue
if s.minScore == 0 and s.avgScore == 0 and s.maxScore == 0: continue
hope = 0
if not s.minScore == 0:
if s.year == year_int - 1:
if last1 > s.minScore:
is_filter = False
hope = 3
elif s.year == year_int - 2:
if last2 > s.minScore:
is_filter = False
hope = 2
elif s.year == year_int - 3:
if last3 > s.minScore:
is_filter = False
hope = 1
if not s.avgScore == 0:
if s.year == year_int - 1:
if last1 > s.avgScore:
is_filter = False
hope = 6
elif s.year == year_int - 2:
if last2 > s.avgScore:
is_filter = False
hope = 5
elif s.year == year_int - 3:
if last3 > s.avgScore:
is_filter = False
hope = 4
if not s.maxScore == 0:
if s.year == year_int - 1:
if last1 > s.maxScore:
is_filter = False
hope = 9
elif s.year == year_int - 2:
if last2 > s.maxScore:
is_filter = False
hope = 8
elif s.year == year_int - 3:
if last3 > s.maxScore:
is_filter = False
hope = 7
if is_filter: continue
school_set.add(s.school + str(s.year))
s.hope = hope
s.hot = universityInfoDict[s.school].hot
result.append(s)
return result
def filter_university_by_major_score():
return filter_university(majorScores)
def filter_university_by_province_score():
return filter_university(provinceScores)
def evaluate_three_year_score():
# 换算该学生在往年的分数
# 粗暴的算法
# 计算 考生的分数在今年高考划线的比值
# 如 该生文科393 ，2017 划线 489,380,300 ，比值分别是393/489=0.803,393/380=1.034,393/300=1.31
# 2016 年 划线 501,403,319,评估得分为(501*0.803+403*1.034+319*1.31)/3=412.3,评估该生在2016分数为412.3
result = {}
year_int = int(year)
score1 = scoreLines[str(year_int) + ',' + regionCode + ',' + subject + ',10036'].score
score2 = scoreLines[str(year_int) + ',' + regionCode + ',' + subject + ',10037'].score
score3 = scoreLines[str(year_int) + ',' + regionCode + ',' + subject + ',10038'].score
tier = ''
if score > score3:
tier = '10038'
if score > score2:
tier = '10037'
if score > score1:
tier = '10036'
# if '' == tier:
# print 'error....'
# return
rate1 = score / float(score1)
rate2 = score / float(score2)
rate3 = score / float(score3) # 福建地区没有三本划线，取专科划线
# 改进算法，分数所在批次权重为0.7,其他为0.15
# TODO 考虑结合一分一段排名表来评估分数，暂时没有找到数据，待完成
last1 = evaluate_score(year_int, regionCode, subject, tier, 1, rate1, rate2, rate3)
result[year_int - 1] = last1
print '评估' + str(year_int - 1) + '分数为：' + str(last1)
last2 = evaluate_score(year_int, regionCode, subject, tier, 2, rate1, rate2, rate3)
result[year_int - 2] = last2
print '评估' + str(year_int - 2) + '分数为：' + str(last2)
last3 = evaluate_score(year_int, regionCode, subject, tier, 3, rate1, rate2, rate3)
result[year_int - 3] = last3
print '评估' + str(year_int - 3) + '分数为：' + str(last3)
print '%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'
return result
def evaluate_score(year, region, subject, tier, n, rate1, rate2, rate3):
if '10036' == tier:
last1score1 = scoreLines[str(year - n) + ',' + region + ',' + subject + ',10036'].score * rate1 * 0.7
else:
last1score1 = scoreLines[str(year - n) + ',' + region + ',' + subject + ',10036'].score * rate1 * 0.15
if '10037' == tier:
last1score2 = scoreLines[str(year - n) + ',' + region + ',' + subject + ',10037'].score * rate2 * 0.7
else:
last1score2 = scoreLines[str(year - n) + ',' + region + ',' + subject + ',10037'].score * rate2 * 0.15
if '10038' == tier:
last1score3 = scoreLines[str(year - n) + ',' + region + ',' + subject + ',10038'].score * rate3 * 0.7
else:
last1score3 = scoreLines[str(year - n) + ',' + region + ',' + subject + ',10038'].score * rate3 * 0.15
if '' == tier:
last1score1 = scoreLines[str(year - n) + ',' + region + ',' + subject + ',10036'].score * rate1 * 0.3
last1score2 = scoreLines[str(year - n) + ',' + region + ',' + subject + ',10037'].score * rate2 * 0.3
last1score3 = scoreLines[str(year - n) + ',' + region + ',' + subject + ',10038'].score * rate3 * 0.4
return last1score1 + last1score2 + last1score3
def save(title, university, xlsx):
print '筛选结果如下，结果将保存到' + xlsx
print '-----------------------------------------------------------------------------------------------------'
print title
print '-----------------------------------------------------------------------------------------------------'
# 筛选结果保存到xls
wb = Workbook()
# 获取当前活跃的worksheet,默认就是第一个worksheet
ws = wb.active
column_names = title.split('\t')
for i in range(1, len(column_names) + 1):
ws.cell(row=1, column=i).value = column_names[i - 1]
row = 2
for u in university:
if u.tier in filterTier: continue
col_content = universityInfoDict[u.school].name + '\t'
if isinstance(u, MajorScore):
col_content = col_content + u.majorName + '\t'
col_content = col_content + universityInfoDict[u.school].region + '\t' + \
universityInfoDict[u.school].classes + '\t' + str(
universityInfoDict[u.school].classRank) + '\t' + str(u.hot) + '\t' + str(u.hope) + '\t' + str(
u.maxScore) + '\t' + str(u.minScore) + '\t' + str(u.avgScore) + '\t' + customCodeDict[u.tier] + '\t' + str(
u.year) + '\t' + str(evaluate_score[u.year])[0:5]
print col_content
row_values = col_content.split('\t')
for col in range(1, len(column_names) + 1):
ws.cell(row=row, column=col).value = row_values[col - 1]
row = row + 1
# 保存
wb.save(filename=xlsx)
def save_xlsx():
t = str(time.time())
save('学校\t地区\t类别\t类别排名\t热度排名\t录取成功预测值（1-9）\t 最高分\t最低分\t平均分\t批次\t年份\t考生评估分数', universityListByProvinceScore,
'./resource/result/result_by_province_score' + t[0:10] + '.xlsx')
save('学校\t专业\t地区\t类别\t类别排名\t热度排名\t录取成功预测值（1-9）\t 最高分\t最低分\t平均分\t批次\t年份\t考生评估分数', universityListByMajorScore,
'./resource/result/result_by_major_score' + t[0:10] + '.xlsx')
def init_custom_code():
code_dict = {'10035': '理科', '10034': '文科', '10036': '一本', '10037': '二本', '10038': '三本', '10148': '专科',
'10149': '提前'}
return code_dict
def help(region):
print '输入参数：省份（行政区代码） 文理科 考生分数 年份 过滤批次'
print '省份代码对应如下'
for k in region:
print k + '-' + region[k]
print '文科-10034，理科10035'
print '10036 一本,10037 二本,10038 三本,10148 专科'
sys.exit(-1)
# 10035 理科
# 10034 文科
#
# 10036 一本
# 10037 二本
# 10038 三本
# 10148 专科
# 10149 提前
if __name__ == "__main__":
regionCodeDict = init_cvs_kv('./resource/region_code.csv', False)
codeRegionDict = init_cvs_kv('./resource/region_code.csv', True)
codeMajorDict = init_cvs_kv('./resource/major_code.csv', False)
majorCodeDict = init_cvs_kv('./resource/major_code.csv', True)
customCodeDict = init_custom_code()
# print str(len(sys.argv))
if len(sys.argv) < 5:
help(regionCodeDict)
filterTier = ''
if len(sys.argv) > 5:
filterTier = sys.argv[5].split(',')
regionCode = sys.argv[1] # region_code 行政区代码
subject = sys.argv[2] # arts_or_science_code 文理科
score = int(sys.argv[3]) # 考生分数
year = sys.argv[4] # 年份
# tierCode = sys.argv[3] # tier_code 本科层次（一本，二本，三本，提前，专科）
print '%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'
print '年份：' + year
print '地区：' + codeRegionDict[regionCode]
print '分数：' + sys.argv[3] + ' ' + customCodeDict[subject]
if not filterTier == '':
for f in filterTier:
print '过滤：' + customCodeDict[f]
print '%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'
universityInfoDict = load_university_info()
print '加载高校库完成，共有' + str(len(universityInfoDict)) + '所高校信息载入'
print '%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'
print '抓取高校库中所有高校在[' + codeRegionDict[regionCode] + ']地区[' + customCodeDict[subject] + ']招生分数线'
spider_university_province_score_line('10036', '本一批次')
spider_university_province_score_line('10037', '本二批次')
spider_university_province_score_line('10038', '本三批次')
spider_university_province_score_line('10148', '高职专科批次')
spider_university_province_score_line('10149', '提前批次')
print '%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'
print '抓取高校库中所有高校在[' + codeRegionDict[regionCode] + ']地区[' + customCodeDict[subject] + ']专业分数线'
spider_university_major_score_line('')
print '%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'
print '载入[' + codeRegionDict[regionCode] + ']地区历年高考划线'
scoreLines = load_score_line() # 历年分数线
print '载入全国高校在[' + codeRegionDict[regionCode] + ']地区[' + customCodeDict[subject] + ']历年录取分数线'
provinceScores = load_province_score() # 各学校入取分数
print '载入全国高校在[' + codeRegionDict[regionCode] + ']地区[' + customCodeDict[subject] + ']历年录取专业分数线'
majorScores = load_major_score() # 各学校各专业录取分数
print '%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'
# 评估分数
evaluate_score = evaluate_three_year_score()
# 筛选高校
universityListByProvinceScore = filter_university_by_province_score()
universityListByMajorScore = filter_university_by_major_score()
# 保存到 xlsx
save_xlsx()