系统性能测试工具 / System Performance Testing Tool 🚀✨

系统性能测试工具 是一个基于 Python 的应用程序，用于全面测试计算机系统的性能，包括 CPU、GPU、内存、磁盘和网络。通过运行一系列基准测试，该工具能够生成详细的性能报告，并给出综合评分。😎📊

System Performance Testing Tool is a Python-based application designed to comprehensively test the performance of computer systems, including CPU, GPU, memory, disk, and network. By running a series of benchmarks, the tool generates detailed performance reports and provides an overall score. 🎯📈

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功能特性 / Features ✨🌟

• CPU 性能测试：通过多线程压力测试、矩阵运算和素数计算评估 CPU 性能。💻🔥
  CPU Performance Test: Evaluates CPU performance through multi-threaded stress tests, matrix operations, and prime number calculations. 🧮💪

• GPU 性能测试：利用 PyTorch 进行矩阵乘法和深度学习模型训练，测试 GPU 性能。🎮🤖
  GPU Performance Test: Tests GPU performance using PyTorch for matrix multiplication and deep learning model training. 🤖🚀

• 内存性能测试：通过随机访问和并发访问测试内存性能。🧠⚡
  Memory Performance Test: Tests memory performance through random access and concurrent access. 🧬💨

• 磁盘性能测试：通过随机读写操作评估磁盘性能。💾📂
  Disk Performance Test: Evaluates disk performance through random read/write operations. 📂🚀

• 网络性能测试：通过访问多个网站测试网络响应速度。🌐📶
  Network Performance Test: Tests network response speed by accessing multiple websites. 🚀🌍

• 综合评分：根据各项测试结果生成综合评分和性能等级（S/A/B/C/D）。📊🏆
  Overall Score: Generates an overall score and performance grade (S/A/B/C/D) based on test results. 🏅📈

• 日志记录：自动生成日志文件，记录测试过程和结果。📝📜
  Logging: Automatically generates log files to record the testing process and results. 📜🔍

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安装与使用 / Installation and Usage 🛠️🔧

1. 环境要求 / Requirements

• Python 3.8 或更高版本 / Python 3.8 or higher 🐍✨
• 以下 Python 库 / The following Python libraries:

  pip install psutil numpy torch requests

2. 克隆仓库 / Clone the Repository

git clone https://github.com/TechnologyStar/System-Performance-Testing-Tool.git
cd System-Performance-Testing-Tool

3. 运行测试工具 / Run the Testing Tool

• 中文版 / Chinese Version:

  python main_zh.py

• 英文版 / English Version:

  python main_en.py

4. 查看结果 / View Results

测试完成后，工具会输出以下内容：🎉📄
After the test is completed, the tool will output the following: 🎊📋

• 各项性能测试的得分。📈💯
  Scores for each performance test.
• 综合评分和性能等级。🏅📊
  Overall score and performance grade.
• 日志文件（位于项目根目录）。📁📜
  Log files (located in the project root directory).

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代码结构 / Code Structure 📂🔍

System-Performance-Testing-Tool/
├── main_en.py             # 英文版主程序 / English version main program 🌍
├── main_zh.py             # 中文版主程序 / Chinese version main program 🇨🇳
├── README.md              # 项目介绍 / Project introduction 📖✨

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示例输出 / Example Output 📄👀

运行测试工具后，你会看到类似以下的输出：👀👇
After running the testing tool, you will see output similar to the following: 📋✨

系统性能测试开始... / System performance testing started... 🚀🔥

正在执行CPU性能测试... / Running CPU performance test... 💻⚡
CPU性能测试完成！ / CPU performance test completed! ✅🎉

正在执行GPU性能测试... / Running GPU performance test... 🎮🤖
GPU性能测试完成！ / GPU performance test completed! ✅🚀

正在执行内存性能测试... / Running memory performance test... 🧠💨
内存性能测试完成！ / Memory performance test completed! ✅⚡

正在执行磁盘性能测试... / Running disk performance test... 💾📂
磁盘性能测试完成！ / Disk performance test completed! ✅🚀

正在执行网络性能测试... / Running network performance test... 🌐📶
网络性能测试完成！ / Network performance test completed! ✅🌍

==================================================
                    测试结果 / Test Results 📊🏆
==================================================
CPU性能得分：      8,500 / CPU Score: 8,500
GPU性能得分：      9,200 / GPU Score: 9,200
内存性能得分：     7,800 / Memory Score: 7,800
磁盘性能得分：     6,500 / Disk Score: 6,500
网络性能得分：     8,000 / Network Score: 8,000
--------------------------------------------------
总体性能得分：     7,800 / Overall Score: 7,800
==================================================

系统性能等级：A / System Performance Grade: A 🏅✨

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贡献指南 / Contribution Guidelines 🤝🌟

我们欢迎任何形式的贡献！如果你有改进建议或发现了问题，请按照以下步骤操作：🙌🛠️
We welcome contributions of any kind! If you have suggestions for improvements or find any issues, please follow these steps: 🚀💡

1. Fork 本仓库。 / Fork this repository. 🍴✨
2. 创建一个新的分支（git checkout -b feature/你的功能）。
   Create a new branch (git checkout -b feature/your-feature). 🌿🔧
3. 提交你的更改（git commit -m '添加了新功能'）。
   Commit your changes (git commit -m 'Add a new feature'). 💡📝
4. 推送到分支（git push origin feature/你的功能）。
   Push to the branch (git push origin feature/your-feature). 🚀📤
5. 提交 Pull Request。
   Submit a Pull Request. 📥🌟

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许可证 / License 📜🔖

本项目基于 MIT 许可证 开源。
This project is open source under the MIT License.

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联系作者 / Contact Author 📧😊

如果你有任何问题或建议，欢迎联系我：📩✨
If you have any questions or suggestions, feel free to contact me: 📧🌟

• GitHub: TechnologyStars 🚀🌟

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致谢 / Acknowledgments 🙏🌈

感谢以下开源项目的支持：🌟🚀
Thanks to the following open-source projects for their support: 🌈✨

• PyTorch
• NumPy
• psutil