def init_workmate_cost():
"""
函数功能：从MySQL获取数据，在所有'workmate_with'关系中设置代价属性'cost'，其值为(type_int+1)/(5*(1+ln(n)))，其中n为同校年数。
type_int同事关系类型[0,10]，n：表示x是y的第n层上级，0：表示x与y是同一级别。
:return: True
"""
print(time_now(1),
f":************************ Workmate ************************")
select_sql = "SELECT id_x, id_y, start_time, end_time, type_int FROM demo.workmate;"
my_cur = mysql.cursor()
my_cur.execute(select_sql)
count = 0
for line in my_cur.fetchall(): # 对MySQL中的每条同事记录进行处理
count += 1
print(time_now(1), count, line)
node1 = graph.data(
f"MATCH (person:Person) WHERE person.id='{line[0]}' RETURN person")
node2 = graph.data(
f"MATCH (person:Person) WHERE person.id='{line[1]}' RETURN person")
if node1 and node2:
node1 = node1[0]['person']
node2 = node2[0]['person']
overlap = int(line[3][0:4]) - int(line[2][0:4])
years = 1 if overlap <= 1 else overlap # 同校年数
cost = (float(line[4]) + 1) / (5 * (1 + math.log(years, math.e))
) # 设置代价属性'cost'
create_relationship(node1, node2, 'workmate_with', 'cost',
cost) # 创建同事关系
return True
def init_countrymen_cost():
"""
函数功能：连接在同一'Location'节点上的两条'is_from'关系相当于一个同乡关系，在所有'is_from'关系中设置代价属性'cost'，其值为5/(3*type_int*2)。
type_int同乡关系类型（1-10）：中国节点上的同乡关系类型为1，地区节点每往下一级关系类型加1，最大为10。
:return: True
"""
# 从'中国'节点开始
china = graph.data(
"MATCH (location:Location) WHERE location.name='中国' RETURN location"
)[0]['location']
type_int = 1 # 地区节点层级
cost = float(5 / type_int / 6)
graph.data(f"MATCH {china}<-[r:is_from]-(:Person) SET r.cost={cost};")
print(
time_now(1),
f":************************ {type_int} Level Location ************************"
)
while type_int < 10: # 按地区层级设置代价属性'cost'
type_int += 1
print(
time_now(1),
f":************************ {type_int} Level Location ************************"
)
cost = float(5 / type_int / 6) # 设置代价属性'cost'
location = graph.data(
f"MATCH {china}-[:include_location*{type_int-1}]->(location:Location)"
f"<-[r:is_from]-(:Person) SET r.cost={cost} RETURN location;")
if not location:
break
return True
def init_schoolfellow_cost():
"""
函数功能：从MySQL获取数据，在所有'schoolfellow_with'关系中设置代价属性'cost'，其值为5/(4*(1+ln(n))*(6-type_int))，其中n为同校年数。
type_int同学关系类型(1,2,3,4,5)，1：表示同学院且同级，2：表示同学院不同级但时间有重叠，3：表示不同学院但同级，4：表示不同学院不同级但时间有重叠，5：表示同校的其他情况。
:return: True
"""
print(time_now(1),
f":************************ Schoolfellow ************************")
select_sql = "SELECT id_x, id_y, start_time, end_time, type_int FROM demo.schoolfellow;"
my_cur = mysql.cursor()
my_cur.execute(select_sql)
count = 0
for line in my_cur.fetchall(): # 对MySQL中的每条同学记录进行处理
count += 1
print(time_now(1), count, line)
node1 = graph.data(
f"MATCH (person:Person) WHERE person.id='{line[0]}' RETURN person")
node2 = graph.data(
f"MATCH (person:Person) WHERE person.id='{line[1]}' RETURN person")
if node1 and node2:
node1 = node1[0]['person']
node2 = node2[0]['person']
overlap = int(line[3][0:4]) - int(line[2][0:4])
years = 1 if overlap <= 1 else overlap # 同校年数
cost = 5 / (4 * (1 + math.log(years, math.e)) *
(6 - float(line[4]))) # 设置代价属性'cost'
create_relationship(node1, node2, 'schoolfellow_with', 'cost',
cost) # 创建同学关系
return True
def allShortestPaths(property1_value=None,
property2_value=None,
limit=10,
n_paths=10,
node1_label='Person',
property1='id',
node2_label='Person',
property2='id',
rel_type=None):
"""
函数功能：从图中查找两个目的节点之间的多条最短关系路径。
返回值中relationship_group[i]表示node_group[i]与node_group[i+1]之间的关系，len(relationship_group)=len(node_group)-1，
relationship_group中的‘关系方向’取值为1或0，代表'->'或'<-'。
:param property1_value: 节点1属性值
:param property2_value: 节点2属性值
:param limit: 路径的最大长度，缺省值为10
:param n_paths: 返回路径的最多条数，缺省值为10
:param node1_label: 节点1标签，缺省值为'Person'
:param property1: 节点1属性，缺省值为'id'
:param node2_label: 节点2标签，缺省值为'Person'
:param property2: 节点2属性，缺省值为'id'
:param rel_type: 允许搜索的关系类型列表，缺省值为None表示允许搜索所有关系类型
:return: 所有路径信息：多条路径节点ID列表node_group，多条路径关系类型列表relationship_group，多条路径关系方向列表direction_group。
"""
last_node = None
node_group = [] # 用来保存所有路径节点的列表
relationship_group = [] # 用来保存所有路径关系的列表
direction_group = [] # 用来保存所有路径关系方向的列表
node1 = graph.find_one(label=node1_label,
property_key=property1,
property_value=property1_value) # 查找节点1
node2 = graph.find_one(label=node2_label,
property_key=property2,
property_value=property2_value) # 查找节点2
get_data = graph.data(
f"MATCH path=allShortestPaths({node1}-[{rel_type} *..{limit}]-{node2}) RETURN path;"
) # 查找路径
i = 0
for results in get_data: # 拆解路径信息
i += 1
print(time_now(1), i, list(walk(results['path'])))
nodes, relationships, directions = [], [], []
for result in walk(results['path']):
if type(result) is types.Node:
last_node = result
nodes.append(result['id']) # 保存当前路径的一个节点
if type(result) is types.Relationship:
relationships.append(result.type()) # 保存当前路径的一个关系名称及方向
directions.append(1 if result.start_node() == last_node else 0)
node_group.append(nodes) # 保存当前路径的所有节点
relationship_group.append(relationships) # 保存当前路径的所有关系名称
direction_group.append(directions) # 保存当前路径的所有关系方向
return node_group[0:n_paths], relationship_group[
0:n_paths], direction_group[0:n_paths]
def select_schoolfellow_multi(person_id=None, school_id=None):
"""
函数功能：通过目标人物ID和目标学校ID，在图中查找该人物在该学校的多种校友关系。
返回值resume_pair_int中，校友关系类型(1,2,3,4,5)，1：表示同学院且同级，2：表示同学院不同级但时间有重叠，3：表示不同学院但同级，4：表示不同学院不同级但时间有重叠，5：表示同校的其他情况。
:param person_id: 目的人物ID
:param school_id: 目的学校ID
:return: 记录相关工作经历信息的列表 education_pair_int[目标人物ID，相关人物ID，目标教育经历ID，相关教育经历ID，重叠开始时间， 重叠结束时间，关系类型]
"""
# 目标人物节点
person = graph.find_one(label='Person',
property_key='id',
property_value=person_id)
school = graph.find_one(label='School',
property_key='id',
property_value=school_id)
academies = graph.data(
f"MATCH {person}-[:study_at]->(academy:Academy)<--{school} RETURN academy;"
)
if not person or not school: #如果没有目标人物节点或目标学校节点，则查找失败
return None
education_pair_int = [] #初始化相关教育经历信息列表
if academies: # 如果目标人物在学院节点有过教育经历
for academy in academies:
academy = academy['academy']
selece_education = graph.data(
f"MATCH {person}-[r:study_at]->{academy} RETURN r;")[0]['r']
if not selece_education or selece_education['start_time'][
0] == '0': # 如果没有这条教育经历或者教育经历开始时间年份为‘0’，则跳过该学院
continue
selece_educationid = selece_education['study_id'] # 保存这条教育经历ID
start_time = selece_education['start_time'] # 保存这条教育经历开始时间
# 保存这条教育经历结束时间，如果结束时间年份为‘0’，表示至今，则按格式更改为当前时间
end_time = selece_education['end_time'] if selece_education[
'end_time'][0] != '0' else time_now(0) # 当前日期
# 查找在当前学院学习过的所有人物
resume_group = graph.data(
f"MATCH {person}-[:study_at]->{academy}<-[r:study_at]-(person:Person) RETURN person, r;"
)
for resume in resume_group: # 匹配教育经历时间
if person_id == resume['person']['id'] or resume['r'][
'start_time'][
0] == '0': # 若ID重复或开始时间年份为‘0’(数据不完整)，则忽略该条记录
continue
if resume['r']['end_time'][
0] == '0': # 结束时间年份为‘0’的表示至今，按格式更改为当前时间
resume['r']['end_time'] = time_now(0) # 当前日期
# 匹配时间段重叠情况
overlap = period_cmp(start_time, end_time,
resume['r']['start_time'],
resume['r']['end_time'])
if overlap:
if start_time[0:4] == resume['r']['start_time'][
0:4]: # 添加关系类型1：同学院且同级
education_pair_int.append([
person_id, resume['person']['id'],
selece_educationid, resume['r']['study_id'],
overlap[0], overlap[1], 1
])
else: # 添加关系类型2：同学院不同级但时间有重叠
education_pair_int.append([
person_id, resume['person']['id'],
selece_educationid, resume['r']['study_id'],
overlap[0], overlap[1], 2
])
else: # 添加关系类型5：同校的其他情况
education_pair_int.append([
person_id, resume['person']['id'], selece_educationid,
resume['r']['study_id'], overlap[0], overlap[1], 5
])
# 查找在当前学校的其他学院学习过的所有人物
resume_group = graph.data(f"MATCH {academy}<-[:include_academy]-(:School)-[:include_academy]->(:Academy)<-[r:study_at]-(person:Person) RETURN person, r;") \
+ graph.data(f"MATCH {academy}<-[:include_academy]-(:School)<-[r:study_at]-(person:Person) RETURN person, r;")
for resume in resume_group: # 匹配教育经历时间
if person_id == resume['person']['id'] or resume['r'][
'start_time'][
0] == '0': # 若ID重复或开始时间年份为‘0’(数据不完整)，则忽略该条记录
continue
if resume['r']['end_time'][
0] == '0': # 结束时间年份为‘0’的表示至今，按格式更改为当前时间
resume['r']['end_time'] = time_now(0) # 当前日期
# 匹配时间段重叠情况
overlap = period_cmp(start_time, end_time,
resume['r']['start_time'],
resume['r']['end_time'])
if overlap:
if start_time[0:4] == resume['r']['start_time'][
0:4]: # 添加关系类型3：不同学院但同级
education_pair_int.append([
person_id, resume['person']['id'],
selece_educationid, resume['r']['study_id'],
overlap[0], overlap[1], 3
])
else: # 添加关系类型4：不同学院不同级但时间有重叠
education_pair_int.append([
person_id, resume['person']['id'],
selece_educationid, resume['r']['study_id'],
overlap[0], overlap[1], 4
])
else: # 添加关系类型5：同校的其他情况
education_pair_int.append([
person_id, resume['person']['id'], selece_educationid,
resume['r']['study_id'], overlap[0], overlap[1], 5
])
# 如果目标人物在学校节点有过教育经历
selece_education = graph.data(
f"MATCH {person}-[r:study_at]->{school} RETURN r;")
if selece_education and not selece_education['start_time'][
0] == '0': # 如果存在这条教育经历并且教育作经历开始时间年份不为‘0’
selece_education = selece_education[0]['r']
selece_educationid = selece_education['study_id'] # 保存这条教育经历ID
start_time = selece_education['start_time'] # 保存这条教育经历开始时间
# 保存这条教育经历结束时间，如果结束时间年份为‘0’，表示至今，则按格式更改为当前时间
end_time = selece_education['end_time'] if selece_education[
'end_time'][0] != '0' else time_now(0) # 当前日期
# 查找在当前学校的其他学院学习过的所有人物
resume_group = graph.data(f"MATCH {school}-[:include_academy]->(:Academy)<-[r:study_at]-(person:Person) RETURN person, r;") \
+ graph.data(f"MATCH {person}-[:study_at]->{school}<-[r:study_at]-(person:Person) RETURN person, r;")
for resume in resume_group: # 匹配教育经历时间
if person_id == resume['person']['id'] or resume['r'][
'start_time'][0] == '0': # 若ID重复或开始时间年份为‘0’(数据不完整)，则忽略该条记录
continue
if resume['r']['end_time'][0] == '0': # 结束时间年份为‘0’的表示至今，按格式更改为当前时间
resume['r']['end_time'] = time_now(0) # 当前日期
# 匹配时间段重叠情况
overlap = period_cmp(start_time, end_time,
resume['r']['start_time'],
resume['r']['end_time'])
if overlap:
if start_time[0:4] == resume['r']['end_time'][
0:4]: # 添加关系类型3：不同学院但同级
education_pair_int.append([
person_id, resume['person']['id'], selece_educationid,
resume['r']['study_id'], overlap[0], overlap[1], 3
])
else: # 添加关系类型4：不同学院不同级但时间有重叠
education_pair_int.append([
person_id, resume['person']['id'], selece_educationid,
resume['r']['study_id'], overlap[0], overlap[1], 4
])
else: # 添加关系类型5：同校的其他情况
education_pair_int.append([
person_id, resume['person']['id'], selece_educationid,
resume['r']['study_id'], overlap[0], overlap[1], 5
])
return education_pair_int # 返回相关教育经历信息列表
def dijkstraWithDefaultWeight(person1_id=None,
person2_id=None,
rel_type_int=[1, 2, 3, 4, 5, 6, 7],
default_weight=10):
"""
函数功能：从图中查找两个人物节点之间的最小代价关系路径。
关系类型[0,7]，0：其他关系类型，1：亲属关系，2：联系人关系，3：同事关系，4：同学关系，5：同乡关系，6：同行人关系，7：关联关系。
返回的字符串中每个节点用‘，’隔开，若有多条路径则每条路径用‘；’隔开。
:param person1_id: 节点1属性值
:param person2_id: 节点2属性值
:param rel_type_int: 允许搜索的关系类型整数列表，为None时表示允许搜索所有关系类型，缺省值为人物间的7种关系类型
:param default_weight: 关系代价值缺省值为10
:return: 查找失败时返回False，查找成功时返回[节点信息字符串paths_nodes_id,关系类型信息字符串paths_relationships,关系方向类型信息字符串paths_directions,路径权值weight_group]。
"""
if rel_type_int:
rel_define = ':'
for rel in rel_type_int:
if rel == 1:
rel_define += 'kinsfolk_with|'
if rel == 2:
rel_define += 'contact_with|'
if rel == 3:
rel_define += 'workmate_with|'
if rel == 4:
rel_define += 'schoolfellow_with|'
if rel == 5:
rel_define += 'is_from|'
if rel == 6:
rel_define += 'walk_with|'
if rel == 7:
rel_define += 'correlate_with|'
else:
rel_define = ''
rel_define = rel_define[0:-1]
last_node = None
node_group = [] # 用来保存所有路径节点的列表
relationship_group = [] # 用来保存所有路径关系的列表
direction_group = [] # 用来保存所有路径关系方向的列表
weight_group = [] # 用来保存所有路径代价值的列表
get_data = graph.data(
f"MATCH (person1:Person), (person2:Person) WHERE person1.id='{person1_id}' and "
f"person2.id='{person2_id}' CALL apoc.algo.dijkstraWithDefaultWeight(person1,person2,"
f"'{rel_define}','cost',{default_weight}) YIELD path,weight RETURN path,weight;"
) # 查找路径
if not get_data: # 如果路径数据为空则查找失败
return False
i = 0
for results in get_data: # 拆解路径信息
i += 1
print(time_now(1), i, list(walk(results['path'])))
nodes, relationships, directions = [], [], []
for resule in walk(results['path']):
if type(resule) is types.Node:
last_node = resule
nodes.append(resule['id']) # 保存当前路径的一个节点
if type(resule) is types.Relationship:
relationships.append(resule.type()) # 保存当前路径的一个关系名称及方向
directions.append(1 if resule.start_node() == last_node else 0)
node_group.append(nodes) # 保存当前路径的所有节点
relationship_group.append(relationships) # 保存当前路径的所有关系名称
direction_group.append(directions) # 保存当前路径的所有关系方向
weight_group.append(results['weight'])
paths_nodes_id = '' # 初始化节点信息串
paths_relationships = '' # 初始化关系类型信息串
paths_directions = '' # 初始化关系方向类型信息串
for i in range(len(direction_group)):
paths_nodes_id += node_group[i][0]
key = trigger = 0 # 触发器
for j in range(len(direction_group[i])):
type_rel = '0'
if relationship_group[i][j] == 'kinsfolk_with':
type_rel = '1'
if relationship_group[i][j] == 'contact_with':
type_rel = '2'
if relationship_group[i][j] == 'workmate_with':
type_rel = '3'
if relationship_group[i][j] == 'schoolfellow_with':
type_rel = '4'
if relationship_group[i][j] == 'is_from':
type_rel = '5'
trigger += 1
if relationship_group[i][j] == 'walk_with':
type_rel = '6'
if relationship_group[i][j] == 'correlate_with':
type_rel = '7'
if trigger % 2 == 1:
continue
paths_nodes_id += ',' + node_group[i][j + 1]
if key == 0:
paths_relationships += type_rel
paths_directions += str(direction_group[i][j])
key = 1
else:
paths_relationships += ',' + type_rel
paths_directions += ',' + str(direction_group[i][j])
if i < len(direction_group) - 1:
paths_nodes_id += ';'
paths_relationships += ';'
paths_directions += ';'
# print(paths_nodes_id)
# print(paths_relationships)
# print(paths_directions)
return paths_nodes_id, paths_relationships, paths_directions, weight_group
def searchAndSave_allShortestPaths(person1_id=None,
person2_id=None,
rel_type_int=[1, 2, 3, 4, 5, 6, 7],
limit=10,
n_paths=10):
"""
函数功能：从图中查找两个人物节点之间的多条最短关系路径，并将结果保存在MySQL数据库中。
关系类型[0,7]，0：其他关系类型，1：亲属关系，2：联系人关系，3：同事关系，4：同学关系，5：同乡关系，6：同行人关系，7：关联关系。
保存的字符串中每个节点用‘，’隔开，若有多条路径则每条路径用‘；’隔开。
:param person1_id: 路径起始节点ID
:param person2_id: 路径终止节点ID
:param rel_type_int: 允许搜索的关系类型整数列表，为None时表示允许搜索所有关系类型，缺省值为人物间的7种关系类型
:param limit: 路径的最大长度，缺省值为10
:param n_paths: 返回路径的最多条数，缺省值为10
:return: 查找失败时返回False，查找成功时返回[节点信息字符串paths_nodes_id,关系类型信息字符串paths_relationships,关系方向类型信息字符串paths_directions]。
"""
my_cur = mysql.cursor() # 获取关系型数据库游标
# MySQL查询语句
select_sql = f"SELECT * FROM demo.paths WHERE start_node_id={person1_id} AND end_node_id={person2_id} " \
f"OR start_node_id={person2_id} AND end_node_id={person1_id};"
# MuSQL更新语句
update_sql = "UPDATE demo.paths SET paths_nodes_id='{0}', paths_relationships='{1}', paths_directions='{2}' " \
"WHERE start_node_id={3} AND end_node_id={4} OR start_node_id={4} AND end_node_id={3};"
# MySQL插入语句，保存路径
insert_sql = "INSERT INTO demo.paths(start_node_id, end_node_id, paths_nodes_id, paths_relationships, paths_directions) " \
"VALUES ('{0}', '{1}', '{2}', '{3}', '{4}');"
if rel_type_int:
rel_define = ':'
for rel in rel_type_int:
if rel == 1:
rel_define += 'kinsfolk_with|'
if rel == 2:
rel_define += 'contact_with|'
if rel == 3:
rel_define += 'workmate_with|'
if rel == 4:
rel_define += 'schoolfellow_with|'
if rel == 5:
rel_define += 'is_from|'
if rel == 6:
rel_define += 'walk_with|'
if rel == 7:
rel_define += 'correlate_with|'
else:
rel_define = ''
rel_define = rel_define[0:-1]
paths = allShortestPaths(person1_id, person2_id, limit, n_paths, 'Person',
'id', 'Person', 'id', rel_define)
if not paths[2]: # 如果路径关系方向类型列表为空则查找失败
return False
paths_nodes_id = '' # 初始化节点信息串
paths_relationships = '' # 初始化关系类型信息串
paths_directions = '' # 初始化关系方向类型信息串
for i in range(len(paths[2])):
paths_nodes_id += paths[0][i][0]
key = trigger = 0 # 触发器
for j in range(len(paths[2][i])):
type_rel = '0'
if paths[1][i][j] == 'kinsfolk_with':
type_rel = '1'
if paths[1][i][j] == 'contact_with':
type_rel = '2'
if paths[1][i][j] == 'workmate_with':
type_rel = '3'
if paths[1][i][j] == 'schoolfellow_with':
type_rel = '4'
if paths[1][i][j] == 'is_from':
type_rel = '5'
trigger += 1
if paths[1][i][j] == 'walk_with':
type_rel = '6'
if paths[1][i][j] == 'correlate_with':
type_rel = '7'
if trigger % 2 == 1:
continue
paths_nodes_id += ',' + paths[0][i][j + 1]
if key == 0:
paths_relationships += type_rel
paths_directions += str(paths[2][i][j])
key = 1
else:
paths_relationships += ',' + type_rel
paths_directions += ',' + str(paths[2][i][j])
if i < len(paths[2]) - 1:
paths_nodes_id += ';'
paths_relationships += ';'
paths_directions += ';'
if my_cur.execute(select_sql):
try:
my_cur.execute(
update_sql.format(paths_nodes_id, paths_relationships,
paths_directions, person1_id, person2_id))
mysql.commit()
print(time_now(1), ':Paths-MySQL Update Successful:', person1_id,
person2_id)
except:
mysql.rollback() # 插入失败，执行回滚操作
print(time_now(1), ':Paths-MySQL Update Error:', person1_id,
person2_id)
else:
try:
my_cur.execute(
insert_sql.format(person1_id, person2_id, paths_nodes_id,
paths_relationships, paths_directions))
mysql.commit()
print(time_now(1), ':Paths-MySQL Insert Successful:', person1_id,
person2_id)
except:
mysql.rollback() # 插入失败，执行回滚操作
print(time_now(1), ':Paths-MySQL Insert Error:', person1_id,
person2_id)
my_cur.close()
return paths_nodes_id, paths_relationships, paths_directions
def select_workmate_multi(person_id=None, position_id=None, max_level=1):
"""
函数功能：通过目标人物ID和目标职位ID，在图中查找该人物的复杂同事关系。
返回值resume_pair_int中，同事关系类型(0-10)，n：表示x是y的第n层上级，0：表示x与y是同一级别。双向关系只保存一条记录。
:param person_id: 目的人物ID
:param position_id: 目的职位ID
:param max_level: 最大允许查找上下级的层数，缺省值为1
:return: 记录相关工作经历信息的列表 resume_pair_int[目标人物ID，相关人物ID，目标工作经历ID，相关工作经历ID，重叠开始时间， 重叠结束时间，关系类型]
"""
# 目标人物节点
resume = graph.find_one(label='Person',
property_key='id',
property_value=person_id)
# 目标职位节点
position = graph.find_one(label='Position',
property_key='id',
property_value=position_id)
# 目标人物在目标职位的工作经历
selece_resume = graph.match_one(start_node=resume,
end_node=position,
rel_type='work_at',
bidirectional=True)
if not selece_resume or selece_resume['start_time'][
0] == '0': # 如果没有这条工作经历或者工作经历开始时间年份为‘0’，则查找失败
return False
selece_resumeid = selece_resume['work_id'] # 保存这条工作经历ID
start_time = selece_resume['start_time'] # 保存这条工作经历开始时间
# 保存这条工作经历结束时间，如果结束时间年份为‘0’，表示至今，则按格式更改为当前时间
end_time = selece_resume[
'end_time'] if selece_resume['end_time'][0] != '0' else time_now(
0) # 当前日期
resume_pair_int = [] # 初始化：相关工作经历信息列表
# 查找在当前职位工作的所有人物
resume_group = graph.data(
f"MATCH {resume}-[:work_at]-{position}-[r:work_at]-(person:Person) RETURN person, r;"
)
for resume in resume_group: # 匹配教育经历时间
if person_id == resume['person']['id'] or resume['r']['start_time'][
0] == '0': # 若ID重复或开始时间年份为‘0’(数据不完整)，则忽略该条记录
continue
if resume['r']['end_time'][0] == '0': # 结束时间年份为‘0’的表示至今，按格式更改为当前时间
resume['r']['end_time'] = time_now(0) # 当前日期
# 匹配时间段重叠情况
overlap = period_cmp(start_time, end_time, resume['r']['start_time'],
resume['r']['end_time'])
if overlap: # 添加相关工作经历信息
resume_pair_int.append([
person_id, resume['person']['id'], selece_resumeid,
resume['r']['work_id'], overlap[0], overlap[1], 0
])
rel_level = 0 # 控制查找层级
while rel_level < max_level: # 按层次依次查找下级职位节点的工作经历
rel_level += 1
persons_down = graph.data(
f"MATCH (person:Person)-[r:work_at]->(position:Position)<-"
f"[:include_position*{rel_level}]-{position} RETURN position, r, person;"
)
if not persons_down[0]: # 当没有更低级职位时退出
break
for resume in persons_down:
for i in range(len(persons_down) - 1):
if selece_resumeid == resume['r']['work_id'] or resume['r'][
'start_time'][
0] == '0': # 若ID重复或开始时间年份为‘0’(数据不完整)，则忽略该条记录
continue
if resume['r']['end_time'][
0] == '0': # 结束时间年份为‘0’的表示至今，按格式更改为当前时间
resume['r']['end_time'] = time_now(0) # 当前日期
# 匹配时间段重叠情况
overlap = period_cmp(start_time, end_time,
resume['r']['start_time'],
resume['r']['end_time'])
if overlap: # 添加相关工作经历信息
resume_pair_int.append([
person_id, resume['person']['id'], selece_resumeid,
resume['r']['work_id'], overlap[0], overlap[1],
rel_level
])
rel_level = 0 # 控制查找层级
while rel_level < max_level: # 按层次依次查找上级职位节点的工作经历
rel_level += 1
persons_up = graph.data(
f"MATCH (person:Person)-[r:work_at]->(position:Position)-"
f"[:include_position*{rel_level}]->{position} RETURN position, r, person;"
)
if not persons_up[0]: # 当没有更高级职位时退出
break
for resume in persons_up:
for i in range(len(persons_up) - 1):
if selece_resumeid == resume['r']['work_id'] or resume['r'][
'start_time'][
0] == '0': # 若ID重复或开始时间年份为‘0’(数据不完整)，则忽略该条记录
continue
if resume['r']['end_time'][
0] == '0': # 结束时间年份为‘0’的表示至今，按格式更改为当前时间
resume['r']['end_time'] = time_now(0) # 当前日期
# 匹配时间段重叠情况
overlap = period_cmp(start_time, end_time,
resume['r']['start_time'],
resume['r']['end_time'])
if overlap: # 添加相关工作经历信息
resume_pair_int.append([
resume['person']['id'], person_id,
resume['r']['work_id'], selece_resumeid,
overlap[0], overlap[1], rel_level
])
return resume_pair_int # 返回相关工作经历信息列表