欢迎参与AI彩票预测系统V4.0的开发！本指南将帮助您了解系统架构、开发环境搭建和贡献流程。
AI-Lottery-Predictor/
├── src/ # 源代码
│ ├── streaming/ # 🌊 实时流处理
│ ├── visualization/ # 🎨 可视化引擎
│ ├── optimization/ # 🧠 智能调优
│ ├── quantum/ # ⚛️ 量子计算
│ ├── ai_assistant/ # 🤖 AI助手
│ ├── ml/ # 🎯 机器学习
│ ├── core/ # 🏗️ 核心功能
│ ├── utils/ # 🔧 工具函数
│ └── gui/ # 🎨 用户界面
├── tests/ # 测试文件
├── docs/ # 文档
├── examples/ # 示例代码
├── scripts/ # 构建脚本
├── requirements.txt # 依赖清单
└── main.py # 主启动文件┌─────────────────────────────────────────────────────────────┐
│ 用户交互层 │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ 🤖 AI助手 │ │ 🎨 3D可视化 │ │ ⚙️ 配置界面 │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
├─────────────────────────────────────────────────────────────┤
│ 智能处理层 │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ 🌊 实时流处理 │ │ 🧠 智能调优 │ │ ⚛️ 量子计算 │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
├─────────────────────────────────────────────────────────────┤
│ 核心引擎层 │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ 🎯 预测引擎 │ │ 📊 分析引擎 │ │ 📈 可视化引擎 │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
├─────────────────────────────────────────────────────────────┤
│ 基础服务层 │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ 🗄️ 数据库 │ │ 🧠 内存优化 │ │ 🛡️ 错误处理 │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
└─────────────────────────────────────────────────────────────┘# 克隆项目
git clone https://github.com/pe0ny9-a/AI-Lottery-Predictor.git
cd AI-Lottery-Predictor
# 创建虚拟环境
python -m venv venv
source venv/bin/activate # Linux/Mac
# 或
venv\Scripts\activate # Windows
# 安装开发依赖
pip install -r requirements.txt
pip install -r requirements-dev.txt# 安装代码格式化工具
pip install black flake8 isort mypy
# 安装测试工具
pip install pytest pytest-asyncio pytest-cov
# 安装预提交钩子
pip install pre-commit
pre-commit install# .vscode/settings.json
{
"python.defaultInterpreterPath": "./venv/bin/python",
"python.linting.enabled": true,
"python.linting.flake8Enabled": true,
"python.formatting.provider": "black",
"python.sortImports.args": ["--profile", "black"],
"editor.formatOnSave": true,
"editor.codeActionsOnSave": {
"source.organizeImports": true
}
}# .env.development
DEBUG=true
LOG_LEVEL=DEBUG
CACHE_ENABLED=true
QUANTUM_ENABLED=true
DATABASE_PATH=data/dev.db
WEBSOCKET_PORT=8765高性能事件驱动的实时数据处理系统
realtime_processor.py - 核心处理引擎stream_buffer.py - 数据缓冲管理event_router.py - 事件路由分发3D交互式图表和动画可视化系统
enhanced_charts.py - 图表引擎chart_types.py - 图表类型定义animation_engine.py - 动画引擎自动化超参数优化和模型选择系统
intelligent_tuner.py - 调优引擎optimizers.py - 优化算法集合parameter_space.py - 参数空间定义量子算法集成和量子机器学习系统
quantum_algorithms.py - 量子算法quantum_circuits.py - 量子线路quantum_ml.py - 量子机器学习from src.streaming.realtime_processor import StreamProcessor, StreamEvent
class CustomEventProcessor(StreamProcessor):
def __init__(self, processor_id: str):
super().__init__(processor_id, self.process_custom_event)
async def process_custom_event(self, event: StreamEvent) -> Any:
"""处理自定义事件"""
try:
# 处理逻辑
result = self.analyze_event_data(event.data)
# 发送处理结果
await self.emit_result_event(result)
return result
except Exception as e:
logger.error(f"处理自定义事件失败: {e}")
raise
# 注册处理器
engine = get_stream_engine()
engine.register_processor(
StreamEventType.CUSTOM_EVENT,
"custom_processor",
CustomEventProcessor("custom_processor").process_custom_event
)from src.visualization.enhanced_charts import InteractiveChart
import plotly.graph_objects as go
class CustomChart(InteractiveChart):
"""自定义图表类型"""
def create_figure(self):
"""创建自定义图表"""
data = self.data.get('custom_data', [])
fig = go.Figure()
# 添加自定义轨迹
fig.add_trace(go.Scatter(
x=[item['x'] for item in data],
y=[item['y'] for item in data],
mode='markers+lines',
name='Custom Data',
marker=dict(
size=10,
color='rgba(255, 182, 193, .9)',
line=dict(width=2, color='DarkSlateGrey')
)
))
# 更新布局
fig.update_layout(
title=self.title,
xaxis_title="X轴标题",
yaxis_title="Y轴标题",
hovermode='closest'
)
self.figure = fig
return fig
# 使用自定义图表
engine = get_visualization_engine()
custom_chart = CustomChart("custom_chart", "自定义图表")
custom_chart.set_data({'custom_data': your_data})
custom_chart.create_figure()def create_custom_theme():
"""创建自定义主题"""
return {
'background_color': '#1a1a1a',
'grid_color': '#404040',
'text_color': '#ffffff',
'color_palette': ['#ff6b6b', '#4ecdc4', '#45b7d1', '#96ceb4'],
'font_family': 'Arial, sans-serif',
'font_size': 12
}
# 注册主题
engine.themes['custom_dark'] = create_custom_theme()
engine.set_theme('custom_dark')from src.quantum.quantum_algorithms import QuantumOptimizer
from qiskit import QuantumCircuit
import numpy as np
class CustomQuantumAlgorithm(QuantumOptimizer):
"""自定义量子算法"""
def optimize(self, objective_function: Callable, **kwargs) -> QuantumResult:
"""执行自定义量子优化"""
start_time = time.time()
try:
# 创建量子线路
circuit = self._create_custom_circuit()
# 执行量子线路
result = self._execute_circuit(circuit)
# 处理结果
optimized_result = self._process_quantum_result(result)
execution_time = time.time() - start_time
return QuantumResult(
algorithm="CustomQuantum",
result=optimized_result,
execution_time=execution_time,
quantum_cost=circuit.depth(),
classical_cost=0,
success_probability=0.85,
metadata={'custom_param': kwargs.get('custom_param')}
)
except Exception as e:
logger.error(f"自定义量子算法执行失败: {e}")
return self._classical_fallback(objective_function, **kwargs)
def _create_custom_circuit(self) -> QuantumCircuit:
"""创建自定义量子线路"""
circuit = QuantumCircuit(self.num_qubits)
# 添加量子门
for i in range(self.num_qubits):
circuit.h(i) # Hadamard门
# 添加纠缠
for i in range(self.num_qubits - 1):
circuit.cx(i, i + 1) # CNOT门
return circuit
# 使用自定义量子算法
qml = get_quantum_ml()
custom_optimizer = CustomQuantumAlgorithm(num_qubits=6)
result = custom_optimizer.optimize(your_objective_function)tests/
├── unit/ # 单元测试
│ ├── test_streaming.py
│ ├── test_visualization.py
│ ├── test_quantum.py
│ └── test_ai_assistant.py
├── integration/ # 集成测试
│ ├── test_system_integration.py
│ └── test_api_integration.py
├── performance/ # 性能测试
│ ├── test_streaming_performance.py
│ └── test_prediction_performance.py
└── fixtures/ # 测试数据
├── sample_data.json
└── mock_responses.pyimport pytest
import asyncio
from src.streaming.realtime_processor import RealtimeStreamEngine
class TestRealtimeStreaming:
"""实时流处理测试"""
@pytest.fixture
async def stream_engine(self):
"""创建测试用的流处理引擎"""
engine = RealtimeStreamEngine(buffer_size=100, worker_count=2)
await engine.start()
yield engine
await engine.stop()
@pytest.mark.asyncio
async def test_event_processing(self, stream_engine):
"""测试事件处理"""
# 准备测试数据
test_event = StreamEvent(
event_id="test_001",
event_type=StreamEventType.DATA_ARRIVAL,
timestamp=time.time(),
data={"test": "data"},
source="test"
)
# 发送事件
await stream_engine.emit_event(test_event)
# 等待处理
await asyncio.sleep(0.1)
# 验证结果
stats = stream_engine.get_system_stats()
assert stats['system']['total_processed'] >= 1
def test_buffer_capacity(self, stream_engine):
"""测试缓冲区容量"""
buffer = stream_engine.buffer
# 测试缓冲区限制
for i in range(150): # 超过缓冲区大小
event = StreamEvent(f"test_{i}", StreamEventType.DATA_ARRIVAL,
time.time(), {}, "test")
buffer.add(event)
assert len(buffer.buffer) <= buffer.max_size# 运行所有测试
pytest
# 运行特定测试文件
pytest tests/unit/test_streaming.py
# 运行带覆盖率的测试
pytest --cov=src tests/
# 运行性能测试
pytest tests/performance/ -v
# 运行异步测试
pytest -m asyncio# 构建镜像
docker build -t ai-lottery-predictor:v4.0 .
# 运行容器
docker run -d \
--name ai-lottery-predictor \
-p 8000:8000 \
-p 8765:8765 \
-v $(pwd)/data:/app/data \
-v $(pwd)/logs:/app/logs \
ai-lottery-predictor:v4.0
# 使用Docker Compose
docker-compose up -d# .env.production
DEBUG=false
LOG_LEVEL=INFO
CACHE_ENABLED=true
QUANTUM_ENABLED=true
GPU_ENABLED=false
DATABASE_PATH=/app/data/production.db
WEBSOCKET_PORT=8765
MAX_WORKERS=8
RATE_LIMIT=1000# 日志配置
logging:
version: 1
formatters:
default:
format: '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
handlers:
console:
class: logging.StreamHandler
formatter: default
file:
class: logging.handlers.RotatingFileHandler
filename: /app/logs/app.log
maxBytes: 10485760 # 10MB
backupCount: 5
formatter: default
root:
level: INFO
handlers: [console, file]git checkout -b feature/your-feature# 代码格式化
black src/ tests/
# 导入排序
isort src/ tests/
# 代码检查
flake8 src/ tests/
# 类型检查
mypy src/# 格式: type(scope): description
feat(streaming): add new event processing algorithm
fix(quantum): resolve QAOA optimization bug
docs(api): update API documentation
test(visualization): add unit tests for chart engine
refactor(core): improve error handling mechanism