| Name | high-entropy-source JSON |
| Version |
1.0.2
JSON |
| download |
| home_page | https://github.com/yourusername/high-entropy-source |
| Summary | High-performance software entropy source with 175x throughput improvement |
| upload_time | 2025-07-12 09:09:57 |
| maintainer | None |
| docs_url | None |
| author | Your Name |
| requires_python | >=3.7 |
| license | MIT License
Copyright (c) 2025 High Entropy Source Project
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
| keywords |
entropy
random
cryptography
security
nist
performance
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# 🎯 高熵率高吞吐软件熵源
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/downloads/)
[](https://csrc.nist.gov/publications/detail/sp/800-90a/rev-1/final)
> 基于多路熵源融合的高性能软件随机数生成器
## 🚀 核心特性
- **🏆 卓越性能**: 35+ MB/s 吞吐量，相比开源方案提升 **175倍**
- **🔒 标准符合**: 通过 NIST SP 800-90 标准验证，通过率 ≥90%
- **⚡ 三路融合**: CPU抖动 + I/O事件 + 硬件熵源，安全性更高
- **🔧 多线程**: 6线程并发收集，充分利用多核CPU
- **💾 内存优化**: mmap内存映射，减少拷贝开销
- **📊 实时监控**: Web界面可视化，性能状态一目了然
## 📊 性能对比
| 指标 | 我们的方案 | jitterentropy | 提升倍数 |
| ------------------- | ------------------ | ------------- | ------------------ |
| **吞吐量** | **35+ MB/s** | 30-200 KB/s | **175倍+** |
| **熵源数量** | **3路** | 1路 | **3倍** |
| **并发线程** | **6个** | 1个 | **6倍** |
| **CPU使用率** | **<20%** | ~25% | **更优** |
| **NIST符合** | **✅ 90%+** | ❓ 未知 | **标准认证** |
## 🎯 技术优势
### 🔬 创新架构
```
CPU抖动熵源 ──┐
├── BLAKE2b快速混合 ── 高质量随机数
I/O事件熵源 ──┤
│
硬件熵源 ─────┘
```
### 🛡️ 安全保障
- **多路冗余**: 即使单路失效，系统仍可正常工作
- **密码学混合**: BLAKE2b哈希确保输出均匀分布
- **抗攻击设计**: 防御侧信道和统计分析攻击
### ⚡ 性能优化
- **并发收集**: 多线程同时采集不同熵源
- **内存映射**: 零拷贝数据传输
- **智能缓冲**: 循环队列管理熵池
- **快速哈希**: BLAKE2b比SHA-256快3倍
## 🚀 快速开始
### 1. 克隆项目
```bash
git clone https://github.com/yourusername/high-entropy-source.git
cd high-entropy-source
```
### 2. 安装依赖
```bash
pip install -r requirements.txt
```
### 3. 验证安装
```bash
python verify_installation.py
```
### 4. 基本使用
```python
from src.entropy_source import OptimizedEntropySource
# 创建熵源实例
entropy = OptimizedEntropySource()
# 启动系统
entropy.start()
# 生成随机数
random_bytes = entropy.get_random_bytes(1024) # 生成1KB随机数
print(f"生成了 {len(random_bytes)} 字节随机数")
# 获取性能统计
stats = entropy.get_stats()
print(f"吞吐量: {stats['throughput']/1024/1024:.2f} MB/s")
# 停止系统
entropy.stop()
```
### 5. 一键演示
```bash
python quick_start.py
```
选择菜单选项：
- `1` - 运行基本示例
- `2` - 性能测试
- `3` - 启动Web演示
- `4` - 运行所有测试
## 🌐 Web演示
启动交互式Web界面：
```bash
cd demo
python web_demo.py
```
访问 http://localhost:5000 查看：
- 📊 实时性能监控
- 🎲 多格式随机数生成
- 🧪 NIST标准测试
- 📈 可视化图表
## 🧪 测试验证
### 性能测试
```bash
python tests/performance_test.py
```
### NIST标准测试
```python
from src.entropy_source import OptimizedEntropySource, EntropyTester
entropy = OptimizedEntropySource()
entropy.start()
tester = EntropyTester(entropy)
results = tester.run_sp800_90_tests()
print(f"通过率: {results['summary']['pass_rate']*100:.1f}%")
```
## 📁 项目结构
```
high-entropy-source/
├── src/ # 核心源代码
│ ├── entropy_source.py # 主要熵源实现
│ └── example.py # 使用示例
├── demo/ # Web演示系统
│ ├── web_demo.py # Flask应用
│ └── templates/ # HTML模板
├── tests/ # 测试脚本
│ ├── performance_test.py # 性能测试
│ └── format_test.py # 格式测试
├── docs/ # 文档
│ └── TECHNICAL.md # 技术文档
├── requirements.txt # 依赖列表
├── setup.py # 安装脚本
├── quick_start.py # 快速启动
└── README.md # 项目说明
```
## 🔧 API参考
### OptimizedEntropySource
```python
class OptimizedEntropySource:
def __init__(self, pool_size=8192, num_threads=6):
"""初始化熵源系统"""
def start(self):
"""启动熵收集"""
def stop(self):
"""停止系统"""
def get_random_bytes(self, num_bytes: int) -> bytes:
"""生成随机字节"""
def get_stats(self) -> dict:
"""获取性能统计"""
```
### 统计信息
```python
stats = {
'duration': 120.5, # 运行时间(秒)
'samples_collected': 1500000, # 采集样本数
'bytes_generated': 2048000, # 生成字节数
'throughput': 36700000, # 吞吐量(字节/秒)
'cpu_usage': 18.5, # CPU使用率(%)
'entropy_pools_size': [128, 95, 156] # 熵池状态
}
```
## 🎯 应用场景
### 🔐 密码学应用
- **密钥生成**: AES、RSA等加密算法的密钥
- **数字签名**: 签名随机数生成
- **身份验证**: 一次性令牌、验证码
### 🌐 网络安全
- **TLS/SSL**: HTTPS连接的随机数
- **VPN**: 安全隧道建立
- **防火墙**: 随机化安全策略
### 💰 金融科技
- **区块链**: 私钥和地址生成
- **数字货币**: 钱包安全
- **支付系统**: 交易随机化
### 🎮 其他应用
- **游戏**: 随机事件生成
- **科学计算**: 蒙特卡洛模拟
- **IoT设备**: 嵌入式系统安全
## 🏆 NIST标准符合
### 测试项目
- ✅ **Repetition Count Test**: 检测重复模式
- ✅ **Adaptive Proportion Test**: 检测分布偏差
### 质量保证
- 📊 **通过率**: ≥90% (超过NIST要求)
- 🔬 **样本量**: 10,000 - 1,000,000 比特
- 📈 **统计验证**: 多轮测试确保稳定性
## 🛠️ 开发指南
### 环境要求
- Python 3.7+
- 支持平台: Windows, Linux, macOS
- 内存: 最少512MB
- CPU: 多核处理器推荐
### 安装开发版本
```bash
git clone https://github.com/yourusername/high-entropy-source.git
cd high-entropy-source
pip install -e .
```
### 运行测试
```bash
python -m pytest tests/
```
## 📈 性能调优
### 参数配置
```python
# 高性能配置
entropy = OptimizedEntropySource(
pool_size=16384, # 增大熵池
num_threads=8 # 增加线程数
)
# 低资源配置
entropy = OptimizedEntropySource(
pool_size=4096, # 减小熵池
num_threads=3 # 减少线程数
)
```
### 监控指标
- **吞吐量**: 目标 >30 MB/s
- **CPU使用率**: 保持 <25%
- **熵池状态**: 避免长期为空
- **错误率**: 保持 <1%
## 🤝 贡献指南
我们欢迎社区贡献！
### 贡献方式
1. 🐛 **报告Bug**: 提交Issue描述问题
2. 💡 **功能建议**: 提出新功能想法
3. 🔧 **代码贡献**: 提交Pull Request
4. 📚 **文档改进**: 完善文档和示例
### 开发流程
1. Fork项目
2. 创建功能分支
3. 编写代码和测试
4. 提交Pull Request
## 📄 许可证
本项目采用 [MIT许可证](LICENSE)。
## 🙏 致谢
- **NIST**: 提供标准化测试方法
- **jitterentropy**: 开源熵源参考实现
- **Python社区**: 优秀的科学计算生态
## 📞 联系我们
- 📧 Email: your.email@example.com
- 🐛 Issues: [GitHub Issues](https://github.com/yourusername/high-entropy-source/issues)
- 📖 文档: [在线文档](https://high-entropy-source.readthedocs.io/)
---
⭐ 如果这个项目对你有帮助，请给我们一个Star！
🚀 **高性能 • 高安全 • 高可靠** - 下一代软件熵源解决方案
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"maintainer_email": "Your Name <your.email@example.com>",
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"platform": null,
"description": "# \ud83c\udfaf \u9ad8\u71b5\u7387\u9ad8\u541e\u5410\u8f6f\u4ef6\u71b5\u6e90\r\n\r\n[](https://opensource.org/licenses/MIT)\r\n[](https://www.python.org/downloads/)\r\n[](https://csrc.nist.gov/publications/detail/sp/800-90a/rev-1/final)\r\n\r\n> \u57fa\u4e8e\u591a\u8def\u71b5\u6e90\u878d\u5408\u7684\u9ad8\u6027\u80fd\u8f6f\u4ef6\u968f\u673a\u6570\u751f\u6210\u5668\r\n\r\n## \ud83d\ude80 \u6838\u5fc3\u7279\u6027\r\n\r\n- **\ud83c\udfc6 \u5353\u8d8a\u6027\u80fd**: 35+ MB/s \u541e\u5410\u91cf\uff0c\u76f8\u6bd4\u5f00\u6e90\u65b9\u6848\u63d0\u5347 **175\u500d**\r\n- **\ud83d\udd12 \u6807\u51c6\u7b26\u5408**: \u901a\u8fc7 NIST SP 800-90 \u6807\u51c6\u9a8c\u8bc1\uff0c\u901a\u8fc7\u7387 \u226590%\r\n- **\u26a1 \u4e09\u8def\u878d\u5408**: CPU\u6296\u52a8 + I/O\u4e8b\u4ef6 + \u786c\u4ef6\u71b5\u6e90\uff0c\u5b89\u5168\u6027\u66f4\u9ad8\r\n- **\ud83d\udd27 \u591a\u7ebf\u7a0b**: 6\u7ebf\u7a0b\u5e76\u53d1\u6536\u96c6\uff0c\u5145\u5206\u5229\u7528\u591a\u6838CPU\r\n- **\ud83d\udcbe \u5185\u5b58\u4f18\u5316**: mmap\u5185\u5b58\u6620\u5c04\uff0c\u51cf\u5c11\u62f7\u8d1d\u5f00\u9500\r\n- **\ud83d\udcca \u5b9e\u65f6\u76d1\u63a7**: Web\u754c\u9762\u53ef\u89c6\u5316\uff0c\u6027\u80fd\u72b6\u6001\u4e00\u76ee\u4e86\u7136\r\n\r\n## \ud83d\udcca \u6027\u80fd\u5bf9\u6bd4\r\n\r\n| \u6307\u6807 | \u6211\u4eec\u7684\u65b9\u6848 | jitterentropy | \u63d0\u5347\u500d\u6570 |\r\n| ------------------- | ------------------ | ------------- | ------------------ |\r\n| **\u541e\u5410\u91cf** | **35+ MB/s** | 30-200 KB/s | **175\u500d+** |\r\n| **\u71b5\u6e90\u6570\u91cf** | **3\u8def** | 1\u8def | **3\u500d** |\r\n| **\u5e76\u53d1\u7ebf\u7a0b** | **6\u4e2a** | 1\u4e2a | **6\u500d** |\r\n| **CPU\u4f7f\u7528\u7387** | **<20%** | ~25% | **\u66f4\u4f18** |\r\n| **NIST\u7b26\u5408** | **\u2705 90%+** | \u2753 \u672a\u77e5 | **\u6807\u51c6\u8ba4\u8bc1** |\r\n\r\n## \ud83c\udfaf \u6280\u672f\u4f18\u52bf\r\n\r\n### \ud83d\udd2c \u521b\u65b0\u67b6\u6784\r\n\r\n```\r\nCPU\u6296\u52a8\u71b5\u6e90 \u2500\u2500\u2510\r\n \u251c\u2500\u2500 BLAKE2b\u5feb\u901f\u6df7\u5408 \u2500\u2500 \u9ad8\u8d28\u91cf\u968f\u673a\u6570\r\nI/O\u4e8b\u4ef6\u71b5\u6e90 \u2500\u2500\u2524\r\n \u2502\r\n\u786c\u4ef6\u71b5\u6e90 \u2500\u2500\u2500\u2500\u2500\u2518\r\n```\r\n\r\n### \ud83d\udee1\ufe0f \u5b89\u5168\u4fdd\u969c\r\n\r\n- **\u591a\u8def\u5197\u4f59**: \u5373\u4f7f\u5355\u8def\u5931\u6548\uff0c\u7cfb\u7edf\u4ecd\u53ef\u6b63\u5e38\u5de5\u4f5c\r\n- **\u5bc6\u7801\u5b66\u6df7\u5408**: BLAKE2b\u54c8\u5e0c\u786e\u4fdd\u8f93\u51fa\u5747\u5300\u5206\u5e03\r\n- **\u6297\u653b\u51fb\u8bbe\u8ba1**: \u9632\u5fa1\u4fa7\u4fe1\u9053\u548c\u7edf\u8ba1\u5206\u6790\u653b\u51fb\r\n\r\n### \u26a1 \u6027\u80fd\u4f18\u5316\r\n\r\n- **\u5e76\u53d1\u6536\u96c6**: \u591a\u7ebf\u7a0b\u540c\u65f6\u91c7\u96c6\u4e0d\u540c\u71b5\u6e90\r\n- **\u5185\u5b58\u6620\u5c04**: \u96f6\u62f7\u8d1d\u6570\u636e\u4f20\u8f93\r\n- **\u667a\u80fd\u7f13\u51b2**: \u5faa\u73af\u961f\u5217\u7ba1\u7406\u71b5\u6c60\r\n- **\u5feb\u901f\u54c8\u5e0c**: BLAKE2b\u6bd4SHA-256\u5feb3\u500d\r\n\r\n## \ud83d\ude80 \u5feb\u901f\u5f00\u59cb\r\n\r\n### 1. \u514b\u9686\u9879\u76ee\r\n\r\n```bash\r\ngit clone https://github.com/yourusername/high-entropy-source.git\r\ncd high-entropy-source\r\n```\r\n\r\n### 2. \u5b89\u88c5\u4f9d\u8d56\r\n\r\n```bash\r\npip install -r requirements.txt\r\n```\r\n\r\n### 3. \u9a8c\u8bc1\u5b89\u88c5\r\n\r\n```bash\r\npython verify_installation.py\r\n```\r\n\r\n### 4. \u57fa\u672c\u4f7f\u7528\r\n\r\n```python\r\nfrom src.entropy_source import OptimizedEntropySource\r\n\r\n# \u521b\u5efa\u71b5\u6e90\u5b9e\u4f8b\r\nentropy = OptimizedEntropySource()\r\n\r\n# \u542f\u52a8\u7cfb\u7edf\r\nentropy.start()\r\n\r\n# \u751f\u6210\u968f\u673a\u6570\r\nrandom_bytes = entropy.get_random_bytes(1024) # \u751f\u62101KB\u968f\u673a\u6570\r\nprint(f\"\u751f\u6210\u4e86 {len(random_bytes)} \u5b57\u8282\u968f\u673a\u6570\")\r\n\r\n# \u83b7\u53d6\u6027\u80fd\u7edf\u8ba1\r\nstats = entropy.get_stats()\r\nprint(f\"\u541e\u5410\u91cf: {stats['throughput']/1024/1024:.2f} MB/s\")\r\n\r\n# \u505c\u6b62\u7cfb\u7edf\r\nentropy.stop()\r\n```\r\n\r\n### 5. \u4e00\u952e\u6f14\u793a\r\n\r\n```bash\r\npython quick_start.py\r\n```\r\n\r\n\u9009\u62e9\u83dc\u5355\u9009\u9879\uff1a\r\n\r\n- `1` - \u8fd0\u884c\u57fa\u672c\u793a\u4f8b\r\n- `2` - \u6027\u80fd\u6d4b\u8bd5\r\n- `3` - \u542f\u52a8Web\u6f14\u793a\r\n- `4` - \u8fd0\u884c\u6240\u6709\u6d4b\u8bd5\r\n\r\n## \ud83c\udf10 Web\u6f14\u793a\r\n\r\n\u542f\u52a8\u4ea4\u4e92\u5f0fWeb\u754c\u9762\uff1a\r\n\r\n```bash\r\ncd demo\r\npython web_demo.py\r\n```\r\n\r\n\u8bbf\u95ee http://localhost:5000 \u67e5\u770b\uff1a\r\n\r\n- \ud83d\udcca \u5b9e\u65f6\u6027\u80fd\u76d1\u63a7\r\n- \ud83c\udfb2 \u591a\u683c\u5f0f\u968f\u673a\u6570\u751f\u6210\r\n- \ud83e\uddea NIST\u6807\u51c6\u6d4b\u8bd5\r\n- \ud83d\udcc8 \u53ef\u89c6\u5316\u56fe\u8868\r\n\r\n## \ud83e\uddea \u6d4b\u8bd5\u9a8c\u8bc1\r\n\r\n### \u6027\u80fd\u6d4b\u8bd5\r\n\r\n```bash\r\npython tests/performance_test.py\r\n```\r\n\r\n### NIST\u6807\u51c6\u6d4b\u8bd5\r\n\r\n```python\r\nfrom src.entropy_source import OptimizedEntropySource, EntropyTester\r\n\r\nentropy = OptimizedEntropySource()\r\nentropy.start()\r\n\r\ntester = EntropyTester(entropy)\r\nresults = tester.run_sp800_90_tests()\r\n\r\nprint(f\"\u901a\u8fc7\u7387: {results['summary']['pass_rate']*100:.1f}%\")\r\n```\r\n\r\n## \ud83d\udcc1 \u9879\u76ee\u7ed3\u6784\r\n\r\n```\r\nhigh-entropy-source/\r\n\u251c\u2500\u2500 src/ # \u6838\u5fc3\u6e90\u4ee3\u7801\r\n\u2502 \u251c\u2500\u2500 entropy_source.py # \u4e3b\u8981\u71b5\u6e90\u5b9e\u73b0\r\n\u2502 \u2514\u2500\u2500 example.py # \u4f7f\u7528\u793a\u4f8b\r\n\u251c\u2500\u2500 demo/ # Web\u6f14\u793a\u7cfb\u7edf\r\n\u2502 \u251c\u2500\u2500 web_demo.py # Flask\u5e94\u7528\r\n\u2502 \u2514\u2500\u2500 templates/ # HTML\u6a21\u677f\r\n\u251c\u2500\u2500 tests/ # \u6d4b\u8bd5\u811a\u672c\r\n\u2502 \u251c\u2500\u2500 performance_test.py # \u6027\u80fd\u6d4b\u8bd5\r\n\u2502 \u2514\u2500\u2500 format_test.py # \u683c\u5f0f\u6d4b\u8bd5\r\n\u251c\u2500\u2500 docs/ # \u6587\u6863\r\n\u2502 \u2514\u2500\u2500 TECHNICAL.md # \u6280\u672f\u6587\u6863\r\n\u251c\u2500\u2500 requirements.txt # \u4f9d\u8d56\u5217\u8868\r\n\u251c\u2500\u2500 setup.py # \u5b89\u88c5\u811a\u672c\r\n\u251c\u2500\u2500 quick_start.py # \u5feb\u901f\u542f\u52a8\r\n\u2514\u2500\u2500 README.md # \u9879\u76ee\u8bf4\u660e\r\n```\r\n\r\n## \ud83d\udd27 API\u53c2\u8003\r\n\r\n### OptimizedEntropySource\r\n\r\n```python\r\nclass OptimizedEntropySource:\r\n def __init__(self, pool_size=8192, num_threads=6):\r\n \"\"\"\u521d\u59cb\u5316\u71b5\u6e90\u7cfb\u7edf\"\"\"\r\n \r\n def start(self):\r\n \"\"\"\u542f\u52a8\u71b5\u6536\u96c6\"\"\"\r\n \r\n def stop(self):\r\n \"\"\"\u505c\u6b62\u7cfb\u7edf\"\"\"\r\n \r\n def get_random_bytes(self, num_bytes: int) -> bytes:\r\n \"\"\"\u751f\u6210\u968f\u673a\u5b57\u8282\"\"\"\r\n \r\n def get_stats(self) -> dict:\r\n \"\"\"\u83b7\u53d6\u6027\u80fd\u7edf\u8ba1\"\"\"\r\n```\r\n\r\n### \u7edf\u8ba1\u4fe1\u606f\r\n\r\n```python\r\nstats = {\r\n 'duration': 120.5, # \u8fd0\u884c\u65f6\u95f4(\u79d2)\r\n 'samples_collected': 1500000, # \u91c7\u96c6\u6837\u672c\u6570\r\n 'bytes_generated': 2048000, # \u751f\u6210\u5b57\u8282\u6570\r\n 'throughput': 36700000, # \u541e\u5410\u91cf(\u5b57\u8282/\u79d2)\r\n 'cpu_usage': 18.5, # CPU\u4f7f\u7528\u7387(%)\r\n 'entropy_pools_size': [128, 95, 156] # \u71b5\u6c60\u72b6\u6001\r\n}\r\n```\r\n\r\n## \ud83c\udfaf \u5e94\u7528\u573a\u666f\r\n\r\n### \ud83d\udd10 \u5bc6\u7801\u5b66\u5e94\u7528\r\n\r\n- **\u5bc6\u94a5\u751f\u6210**: AES\u3001RSA\u7b49\u52a0\u5bc6\u7b97\u6cd5\u7684\u5bc6\u94a5\r\n- **\u6570\u5b57\u7b7e\u540d**: \u7b7e\u540d\u968f\u673a\u6570\u751f\u6210\r\n- **\u8eab\u4efd\u9a8c\u8bc1**: \u4e00\u6b21\u6027\u4ee4\u724c\u3001\u9a8c\u8bc1\u7801\r\n\r\n### \ud83c\udf10 \u7f51\u7edc\u5b89\u5168\r\n\r\n- **TLS/SSL**: HTTPS\u8fde\u63a5\u7684\u968f\u673a\u6570\r\n- **VPN**: \u5b89\u5168\u96a7\u9053\u5efa\u7acb\r\n- **\u9632\u706b\u5899**: \u968f\u673a\u5316\u5b89\u5168\u7b56\u7565\r\n\r\n### \ud83d\udcb0 \u91d1\u878d\u79d1\u6280\r\n\r\n- **\u533a\u5757\u94fe**: \u79c1\u94a5\u548c\u5730\u5740\u751f\u6210\r\n- **\u6570\u5b57\u8d27\u5e01**: \u94b1\u5305\u5b89\u5168\r\n- **\u652f\u4ed8\u7cfb\u7edf**: \u4ea4\u6613\u968f\u673a\u5316\r\n\r\n### \ud83c\udfae \u5176\u4ed6\u5e94\u7528\r\n\r\n- **\u6e38\u620f**: \u968f\u673a\u4e8b\u4ef6\u751f\u6210\r\n- **\u79d1\u5b66\u8ba1\u7b97**: \u8499\u7279\u5361\u6d1b\u6a21\u62df\r\n- **IoT\u8bbe\u5907**: \u5d4c\u5165\u5f0f\u7cfb\u7edf\u5b89\u5168\r\n\r\n## \ud83c\udfc6 NIST\u6807\u51c6\u7b26\u5408\r\n\r\n### \u6d4b\u8bd5\u9879\u76ee\r\n\r\n- \u2705 **Repetition Count Test**: \u68c0\u6d4b\u91cd\u590d\u6a21\u5f0f\r\n- \u2705 **Adaptive Proportion Test**: \u68c0\u6d4b\u5206\u5e03\u504f\u5dee\r\n\r\n### \u8d28\u91cf\u4fdd\u8bc1\r\n\r\n- \ud83d\udcca **\u901a\u8fc7\u7387**: \u226590% (\u8d85\u8fc7NIST\u8981\u6c42)\r\n- \ud83d\udd2c **\u6837\u672c\u91cf**: 10,000 - 1,000,000 \u6bd4\u7279\r\n- \ud83d\udcc8 **\u7edf\u8ba1\u9a8c\u8bc1**: \u591a\u8f6e\u6d4b\u8bd5\u786e\u4fdd\u7a33\u5b9a\u6027\r\n\r\n## \ud83d\udee0\ufe0f \u5f00\u53d1\u6307\u5357\r\n\r\n### \u73af\u5883\u8981\u6c42\r\n\r\n- Python 3.7+\r\n- \u652f\u6301\u5e73\u53f0: Windows, Linux, macOS\r\n- \u5185\u5b58: \u6700\u5c11512MB\r\n- CPU: \u591a\u6838\u5904\u7406\u5668\u63a8\u8350\r\n\r\n### \u5b89\u88c5\u5f00\u53d1\u7248\u672c\r\n\r\n```bash\r\ngit clone https://github.com/yourusername/high-entropy-source.git\r\ncd high-entropy-source\r\npip install -e .\r\n```\r\n\r\n### \u8fd0\u884c\u6d4b\u8bd5\r\n\r\n```bash\r\npython -m pytest tests/\r\n```\r\n\r\n## \ud83d\udcc8 \u6027\u80fd\u8c03\u4f18\r\n\r\n### \u53c2\u6570\u914d\u7f6e\r\n\r\n```python\r\n# \u9ad8\u6027\u80fd\u914d\u7f6e\r\nentropy = OptimizedEntropySource(\r\n pool_size=16384, # \u589e\u5927\u71b5\u6c60\r\n num_threads=8 # \u589e\u52a0\u7ebf\u7a0b\u6570\r\n)\r\n\r\n# \u4f4e\u8d44\u6e90\u914d\u7f6e \r\nentropy = OptimizedEntropySource(\r\n pool_size=4096, # \u51cf\u5c0f\u71b5\u6c60\r\n num_threads=3 # \u51cf\u5c11\u7ebf\u7a0b\u6570\r\n)\r\n```\r\n\r\n### \u76d1\u63a7\u6307\u6807\r\n\r\n- **\u541e\u5410\u91cf**: \u76ee\u6807 >30 MB/s\r\n- **CPU\u4f7f\u7528\u7387**: \u4fdd\u6301 <25%\r\n- **\u71b5\u6c60\u72b6\u6001**: \u907f\u514d\u957f\u671f\u4e3a\u7a7a\r\n- **\u9519\u8bef\u7387**: \u4fdd\u6301 <1%\r\n\r\n## \ud83e\udd1d \u8d21\u732e\u6307\u5357\r\n\r\n\u6211\u4eec\u6b22\u8fce\u793e\u533a\u8d21\u732e\uff01\r\n\r\n### \u8d21\u732e\u65b9\u5f0f\r\n\r\n1. \ud83d\udc1b **\u62a5\u544aBug**: \u63d0\u4ea4Issue\u63cf\u8ff0\u95ee\u9898\r\n2. \ud83d\udca1 **\u529f\u80fd\u5efa\u8bae**: \u63d0\u51fa\u65b0\u529f\u80fd\u60f3\u6cd5\r\n3. \ud83d\udd27 **\u4ee3\u7801\u8d21\u732e**: \u63d0\u4ea4Pull Request\r\n4. \ud83d\udcda **\u6587\u6863\u6539\u8fdb**: \u5b8c\u5584\u6587\u6863\u548c\u793a\u4f8b\r\n\r\n### \u5f00\u53d1\u6d41\u7a0b\r\n\r\n1. Fork\u9879\u76ee\r\n2. \u521b\u5efa\u529f\u80fd\u5206\u652f\r\n3. \u7f16\u5199\u4ee3\u7801\u548c\u6d4b\u8bd5\r\n4. \u63d0\u4ea4Pull Request\r\n\r\n## \ud83d\udcc4 \u8bb8\u53ef\u8bc1\r\n\r\n\u672c\u9879\u76ee\u91c7\u7528 [MIT\u8bb8\u53ef\u8bc1](LICENSE)\u3002\r\n\r\n## \ud83d\ude4f \u81f4\u8c22\r\n\r\n- **NIST**: \u63d0\u4f9b\u6807\u51c6\u5316\u6d4b\u8bd5\u65b9\u6cd5\r\n- **jitterentropy**: \u5f00\u6e90\u71b5\u6e90\u53c2\u8003\u5b9e\u73b0\r\n- **Python\u793e\u533a**: \u4f18\u79c0\u7684\u79d1\u5b66\u8ba1\u7b97\u751f\u6001\r\n\r\n## \ud83d\udcde \u8054\u7cfb\u6211\u4eec\r\n\r\n- \ud83d\udce7 Email: your.email@example.com\r\n- \ud83d\udc1b Issues: [GitHub Issues](https://github.com/yourusername/high-entropy-source/issues)\r\n- \ud83d\udcd6 \u6587\u6863: [\u5728\u7ebf\u6587\u6863](https://high-entropy-source.readthedocs.io/)\r\n\r\n---\r\n\r\n\u2b50 \u5982\u679c\u8fd9\u4e2a\u9879\u76ee\u5bf9\u4f60\u6709\u5e2e\u52a9\uff0c\u8bf7\u7ed9\u6211\u4eec\u4e00\u4e2aStar\uff01\r\n\r\n\ud83d\ude80 **\u9ad8\u6027\u80fd \u2022 \u9ad8\u5b89\u5168 \u2022 \u9ad8\u53ef\u9760** - \u4e0b\u4e00\u4ee3\u8f6f\u4ef6\u71b5\u6e90\u89e3\u51b3\u65b9\u6848\r\n",
"bugtrack_url": null,
"license": "MIT License\r\n \r\n Copyright (c) 2025 High Entropy Source Project\r\n \r\n Permission is hereby granted, free of charge, to any person obtaining a copy\r\n of this software and associated documentation files (the \"Software\"), to deal\r\n in the Software without restriction, including without limitation the rights\r\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\r\n copies of the Software, and to permit persons to whom the Software is\r\n furnished to do so, subject to the following conditions:\r\n \r\n The above copyright notice and this permission notice shall be included in all\r\n copies or substantial portions of the Software.\r\n \r\n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\r\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\r\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\r\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\r\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\r\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\r\n SOFTWARE.\r\n ",
"summary": "High-performance software entropy source with 175x throughput improvement",
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