修改 API 测试程序，更新 README
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README.md
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README.md
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# 数据库配置说明
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# 装备成本预测系统
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本系统使用 MySQL 8.0+ 作为数据库。在安装 MySQL 后，需要：
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基于机器学习的装备成本预测系统，支持多种预测模型和数据分析功能。
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1. 创建数据库用户
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## 功能特性
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```sql
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- 多模型成本预测
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CREATE USER 'equipment_user'@'localhost' IDENTIFIED BY 'your_password';
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- 机器学习模型 (XGBoost, LightGBM, RandomForest)
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GRANT ALL PRIVILEGES ON equipment_cost_db.* TO 'equipment_user'@'localhost';
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- PLS 回归模型
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FLUSH PRIVILEGES;
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- 特征分析与数据可视化
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```
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- 生产商分析
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- 数据集管理
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- 模型训练与评估
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2. 配置数据库字符集
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## 系统要求
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确保 MySQL 配置文件(my.cnf 或 my.ini)包含以下设置：
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```ini
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- Python >= 3.9, < 3.12
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[mysqld]
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- MySQL >= 8.0
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character-set-server=utf8mb4
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- 其他依赖见 pyproject.toml
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collation-server=utf8mb4_unicode_ci
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[client]
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## 快速开始
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default-character-set=utf8mb4
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```
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## 环境配置
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1. 克隆项目
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本项目需要 Python 3.9-3.11 版本。推荐使用 Python 3.11.8。
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### 使用脚本自动配置（推荐）
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Unix/macOS:
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```bash
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```bash
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chmod +x scripts/setup_env.sh
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git clone [repository-url]
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./scripts/setup_env.sh
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cd cost-prediction
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```
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```
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Windows (PowerShell):
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2. 安装依赖
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```powershell
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```bash
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Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
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pip install -e .
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.\scripts\setup_env.ps1
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```
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```
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### 手动配置
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3. 配置数据库
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1. 安装 pyenv
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```bash
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2. 安装 Python 3.11.8:
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[Windows]
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scripts/setup_env.ps1
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```bash
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[Linux/macOS]
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pyenv install 3.11.8
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scripts/setup_env.sh
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```
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```
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3. 设置本地 Python 版本:
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4. 运行系统
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```bash
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```bash
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pyenv local 3.11.8
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python run.py
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```
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```
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4. 创建虚拟环境:
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## API 文档
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```bash
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### 预测接口
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python -m venv .venv
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```
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5. 激活虚拟环境:
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- POST `/api/predict` - 使用最优机器学习模型预测
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- POST `/api/pls/predict` - 使用 PLS 模型预测
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```bash
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### 数据管理
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source .venv/bin/activate # Unix
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.venv\Scripts\activate # Windows
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```
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6. 安装依赖:
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- GET `/api/data` - 获取装备数据列表
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- GET `/api/data/details/<id>` - 获取装备详情
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- PUT `/api/data/<id>` - 更新装备数据
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```bash
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### 数据集管理
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pip install -e ".[dev]"
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```
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- GET `/api/datasets` - 获取数据集列表
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- POST `/api/datasets` - 创建数据集
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- GET `/api/datasets/<id>` - 获取数据集详情
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- PUT `/api/datasets/<id>` - 更新数据集
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- DELETE `/api/datasets/<id>` - 删除数据集
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### 模型管理
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- GET `/api/models` - 获取模型列表
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- POST `/api/train` - 训练模型
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- POST `/api/models/<id>/activate` - 激活模型
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- DELETE `/api/models/<id>` - 删除模型
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### 分析功能
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- POST `/api/analyze-features` - 特征分析
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- POST `/api/analyze-manufacturers` - 生产商分析
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## 开发指南
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详细的开发文档请参考 `docs/dev/` 目录：
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- requirements.md - 项目需求文档
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- debug.md - 调试指南
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## 测试
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运行测试：
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```bash
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python src/test_api.py
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```
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## 许可证
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本项目采用 [LICENSE](LICENSE) 许可证。
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@ -32,6 +32,7 @@ dependencies = [
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# 工具
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# 工具
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"openpyxl>=3.1.5", # Excel支持
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"openpyxl>=3.1.5", # Excel支持
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"python-dotenv>=1.0.0", # 环境变量
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"python-dotenv>=1.0.0", # 环境变量
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"requests>=2.31.0", # API测试
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]
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]
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[project.optional-dependencies]
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[project.optional-dependencies]
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118
src/test_api.py
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src/test_api.py
@ -147,14 +147,124 @@ def test_api_endpoints():
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response = requests.get(f'{base_url}/models/巡飞弹/latest')
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response = requests.get(f'{base_url}/models/巡飞弹/latest')
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print_response(response, "获取最新模型")
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print_response(response, "获取最新模型")
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# 8. 测试多模型预测接口
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# 8. 测试预测接口
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logger.info("\n8. 测试多模型预测接口")
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logger.info("\n8. 测试预测接口")
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# 8.1 测试普通预测接口
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logger.info("8.1 测试普通预测接口")
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predict_data = {
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"type": "巡飞弹",
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"length_m": 1.3,
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"width_m": 0.23,
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"height_m": 0.23,
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"weight_kg": 12.5,
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"max_range_km": 40,
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"max_speed_ms": 50,
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"cruise_speed_kmh": 100,
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"flight_time_min": 60,
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"folded_length_mm": 1300,
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"folded_width_mm": 230,
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"folded_height_mm": 230,
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"warhead_type": "破片杀伤战斗部",
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"launch_mode": "凭自身动力起飞"
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}
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response = requests.post(
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response = requests.post(
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f'{base_url}/predict/all',
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f'{base_url}/predict',
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json=predict_data
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json=predict_data
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)
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)
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print_response(response, "多模型预测")
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print_response(response, "普通预测")
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# 8.2 测试 PLS 预测接口
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logger.info("8.2 测试 PLS 预测接口")
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response = requests.post(
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f'{base_url}/pls/predict',
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json=predict_data
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)
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print_response(response, "PLS 预测")
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# 9. 测试生产商分析接口
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logger.info("\n9. 测试生产商分析接口")
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manufacturer_data = {
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"dataset_id": 1 # 使用已存在的数据集ID
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}
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response = requests.post(
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f'{base_url}/analyze-manufacturers',
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json=manufacturer_data
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)
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print_response(response, "生产商分析")
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# 10. 测试模型激活接口
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logger.info("\n10. 测试模型激活接口")
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# 假设存在ID为1的模型
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response = requests.post(f'{base_url}/models/1/activate')
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print_response(response, "模型激活")
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# 11. 测试获取最新模型接口
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logger.info("\n11. 测试获取最新模型接口")
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response = requests.get(f'{base_url}/models/巡飞弹/latest')
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print_response(response, "获取最新模型")
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# 12. 测试数据集详情接口
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logger.info("\n12. 测试数据集详情接口")
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response = requests.get(f'{base_url}/datasets/1') # 假设存在ID为1的数据集
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print_response(response, "数据集详情")
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# 13. 测试更新数据集接口
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logger.info("\n13. 测试更新数据集接口")
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if available_equipment_ids:
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update_dataset_data = {
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"name": "更新后的测试数据集",
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"description": "用于测试的更新数据集",
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"equipment_type": "巡飞弹",
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"purpose": "测试",
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"equipment_ids": available_equipment_ids[:2] # 使用前两个可用的装备ID
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}
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response = requests.put(
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f'{base_url}/datasets/1', # 假设更新ID为1的数据集
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json=update_dataset_data
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)
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print_response(response, "更新数据集")
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else:
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logger.warning("没有可用的装备ID，跳过数据集更新测试")
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# 14. 测试装备详情接口
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logger.info("\n14. 测试装备详情接口")
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if available_equipment_ids:
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response = requests.get(f'{base_url}/data/details/{available_equipment_ids[0]}')
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print_response(response, "装备详情")
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# 15. 测试更新装备接口
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logger.info("\n15. 测试更新装备接口")
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if available_equipment_ids:
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equipment_update_data = {
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"equipment_id": available_equipment_ids[0],
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"name": "更新后的装备名称",
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"type": "巡飞弹",
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"manufacturer": "测试厂商",
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"length_m": 1.5,
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"width_m": 0.3,
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"height_m": 0.3,
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"weight_kg": 15.0,
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"wingspan_m": 0.8,
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"warhead_weight_kg": 5.0,
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"max_speed_ms": 60,
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"cruise_speed_kmh": 120,
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"endurance_min": 45,
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"max_range_km": 50,
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"warhead_type": "高爆战斗部",
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"launch_mode": "弹射起飞",
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"power_system": "涡轮发动机",
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"guidance_system": "GPS/INS组合导航"
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}
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response = requests.put(
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f'{base_url}/data/{available_equipment_ids[0]}',
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json=equipment_update_data
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)
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print_response(response, "更新装备")
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logger.info("所有测试完成")
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logger.info("所有测试完成")
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except requests.exceptions.RequestException as e:
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except requests.exceptions.RequestException as e:
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