利用python编程实现基于属性的访问控制模型ABAC包括：主体S,客体O,环境E，操作O的集合并写入数据库，主体客体环境属性各有10个，每一个策略中主体，客体环境相关的规则由3-5个属性的布尔表达式组成（随机生成），策略个数共有10000个（正授权5000个，负授权5000个）随机生成，随机生成10000进行决策，记录决策结果。策略库是文本数据库，请求也是文本。

好的，我可以帮你回答这个问题。首先，你需要安装 Python 和相关的数据库模块，比如 SQLite3。然后，你可以创建一个 Python 脚本来实现这个基于属性的访问控制模型 ABAC。

以下是代码示例：

import sqlite3
import random

# 定义主体、客体、环境属性
subjects = ['subject1', 'subject2', 'subject3', 'subject4', 'subject5', 'subject6', 'subject7', 'subject8', 'subject9', 'subject10']
objects = ['object1', 'object2', 'object3', 'object4', 'object5', 'object6', 'object7', 'object8', 'object9', 'object10']
environments = ['environment1', 'environment2', 'environment3', 'environment4', 'environment5', 'environment6', 'environment7', 'environment8', 'environment9', 'environment10']

# 定义策略库
policies = []
for i in range(10000):
    policy = {}
    policy['name'] = 'policy' + str(i)
    policy['subject'] = {}
    for j in range(random.randint(3, 5)):
        attribute = random.choice(subjects)
        value = random.choice([True, False])
        policy['subject'][attribute] = value
    policy['object'] = {}
    for j in range(random.randint(3, 5)):
        attribute = random.choice(objects)
        value = random.choice([True, False])
        policy['object'][attribute] = value
    policy['environment'] = {}
    for j in range(random.randint(3, 5)):
        attribute = random.choice(environments)
        value = random.choice([True, False])
        policy['environment'][attribute] = value
    policy['authorized'] = bool(i < 5000)
    policies.append(policy)

# 将策略库写入数据库
conn = sqlite3.connect('policies.db')
c = conn.cursor()
c.execute('CREATE TABLE policies (name TEXT, subject TEXT, object TEXT, environment TEXT, authorized INTEGER)')
for policy in policies:
    subject = str(policy['subject']).replace("'", '"')
    obj = str(policy['object']).replace("'", '"')
    env = str(policy['environment']).replace("'", '"')
    authorized = 1 if policy['authorized'] else 0
    c.execute(f"INSERT INTO policies VALUES ('{policy['name']}', '{subject}', '{obj}', '{env}', {authorized})")
conn.commit()

# 生成请求并进行决策
decisions = []
for i in range(10000):
    request = {}
    request['subject'] = {}
    for j in range(len(subjects)):
        request['subject'][subjects[j]] = bool(random.getrandbits(1))
    request['object'] = {}
    for j in range(len(objects)):
        request['object'][objects[j]] = bool(random.getrandbits(1))
    request['environment'] = {}
    for j in range(len(environments)):
        request['environment'][environments[j]] = bool(random.getrandbits(1))
    query = "SELECT authorized FROM policies WHERE "
    for attribute, value in request['subject'].items():
        query += f"subject LIKE '%{attribute}:{value}%' AND "
    for attribute, value in request['object'].items():
        query += f"object LIKE '%{attribute}:{value}%' AND "
    for attribute, value in request['environment'].items():
        query += f"environment LIKE '%{attribute}:{value}%' AND "
    query = query[:-5]
    c.execute(query)
    result = c.fetchone()
    decision = True if result and result[0] == 1 else False
    decisions.append(decision)

# 关闭数据库连接
conn.close()

# 输出决策结果
print(decisions)

这段代码会生成一个包含 10000 个请求的列表，其中每个请求都包括主体、客体和环境的属性。然后，它会从策略库中查找与请求匹配的策略，并根据策略中的授权信息做出决策。最后，它会输出一个包含 10000 个决策结果的列表。

请注意，这段代码只是一个示例，你需要根据自己的需求进行修改和完善。同时，你需要在自己的电脑上安装 Python 和 SQLite3，并且创建一个名为 policies.db 的数据库文件。