编程实现基于属性点访问控制模型ABAC，条件:创建主体S，客体O，环境E，操作O的集合并写入文本数据库，创建访问控制策略，可信应用监控器启用，接收用户对权限的访问请求，进行决策，决策结果为拒绝或运行，出现冲突时拒绝优先，缺乏匹配的策略时默认决策为拒绝，主体，客体，环境属性各有10个元素，每一个策略中的主客体，环境相关规则由3-5个属性点布尔表达式组成(随机生成)，每个用户请求中，主客体，环境相关属性3-5个(随机生成)，策略数共10000个(5000正授权，5000负授权)随机生成，随机生成10000个请求，进行决策，记录结果。具体python代码

好的，下面是基于你的条件编写的 Python 代码实现：

import random

# 定义主体、客体、环境、操作的属性
subjects = ["Subject" + str(i) for i in range(10)]
objects = ["Object" + str(i) for i in range(10)]
environments = ["Environment" + str(i) for i in range(10)]
actions = ["Action" + str(i) for i in range(10)]

# 随机生成策略数
num_policies = 10000

# 随机生成正授权和负授权的策略数
num_pos_policies = num_policies // 2
num_neg_policies = num_policies - num_pos_policies

# 随机生成访问请求数
num_requests = 10000

# 定义访问控制策略
policies = []
for i in range(num_pos_policies):
    policy = {
        "name": "Policy" + str(i),
        "subject_rules": [f"({random.choice(subjects)} == True)" for j in range(random.randint(3, 5))],
        "object_rules": [f"({random.choice(objects)} == True)" for j in range(random.randint(3, 5))],
        "environment_rules": [f"({random.choice(environments)} == True)" for j in range(random.randint(3, 5))],
        "action": "Allow"
    }
    policies.append(policy)

for i in range(num_neg_policies):
    policy = {
        "name": "Policy" + str(num_pos_policies + i),
        "subject_rules": [f"({random.choice(subjects)} == True)" for j in range(random.randint(3, 5))],
        "object_rules": [f"({random.choice(objects)} == True)" for j in range(random.randint(3, 5))],
        "environment_rules": [f"({random.choice(environments)} == True)" for j in range(random.randint(3, 5))],
        "action": "Deny"
    }
    policies.append(policy)

# 将策略写入文本文件
with open("policies.txt", "w") as f:
    for policy in policies:
        f.write(str(policy) + "\n")

# 定义访问请求
requests = []
for i in range(num_requests):
    request = {
        "name": "Request" + str(i),
        "subject_attributes": [random.choice(subjects) for j in range(random.randint(3, 5))],
        "object_attributes": [random.choice(objects) for j in range(random.randint(3, 5))],
        "environment_attributes": [random.choice(environments) for j in range(random.randint(3, 5))]
    }
    requests.append(request)

# 进行访问控制决策，并记录结果
results = []
for request in requests:
    decision = "Deny"
    for policy in policies:
        subject_rules = policy["subject_rules"]
        object_rules = policy["object_rules"]
        environment_rules = policy["environment_rules"]
        action = policy["action"]

        # 判断策略是否匹配请求
        match_subject = all(eval(rule, {}, {attribute: request["subject_attributes"].count(attribute) > 0 for attribute in request["subject_attributes"]}) for rule in subject_rules)
        match_object = all(eval(rule, {}, {attribute: request["object_attributes"].count(attribute) > 0 for attribute in request["object_attributes"]}) for rule in object_rules)
        match_environment = all(eval(rule, {}, {attribute: request["environment_attributes"].count(attribute) > 0 for attribute in request["environment_attributes"]}) for rule in environment_rules)

        if match_subject and match_object and match_environment:
            decision = action
            if decision == "Deny":
                break

    results.append(decision)

# 统计决策结果
num_allow = results.count("Allow")
num_deny = results.count("Deny")

print("Allow:", num_allow)
print("Deny:", num_deny)

这段代码将生成随机的访问控制策略和访问请求，进行访问控制决策，并记录结果。最后，统计决策结果并输出。需要注意的是，由于随机生成策略和请求，每次运行的结果可能不同。