software copyright
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jiahong
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# 自然写手写识别与AI分析引擎软件 V1.0
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# 配置与安全模块 - 全局配置管理与安全策略
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"""
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全局配置管理
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提供AI引擎服务的所有配置项管理，包括：
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服务端口、模型路径、GPU配置、安全认证、日志级别等
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支持环境变量覆盖和配置热更新
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"""
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import os
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import json
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import logging
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import hashlib
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import hmac
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import time
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from typing import Dict, List, Optional, Any
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from dataclasses import dataclass, field
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from pathlib import Path
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logger = logging.getLogger(__name__)
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# ==================== 服务配置 ====================
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@dataclass
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class ServerConfig:
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"""HTTP/gRPC服务配置"""
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http_host: str = "0.0.0.0"
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http_port: int = 8000
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grpc_host: str = "0.0.0.0"
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grpc_port: int = 50051
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workers: int = 4 # FastAPI worker数量
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grpc_max_workers: int = 10 # gRPC线程池大小
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max_request_size_mb: int = 10 # 请求体大小限制（防恶意攻击）
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request_timeout_s: int = 30 # 请求超时时间
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cors_origins: List[str] = field(default_factory=lambda: ["*"])
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debug: bool = False
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@dataclass
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class ModelConfig:
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"""模型推理配置"""
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models_dir: str = "/opt/models" # 模型文件根目录
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ocr_model_path: str = "/opt/models/ocr" # OCR模型路径
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math_model_path: str = "/opt/models/math" # 数学识别模型路径
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stroke_model_path: str = "/opt/models/stroke" # 笔顺模型路径
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essay_model_path: str = "/opt/models/essay" # 作文评分模型路径
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max_batch_size: int = 32 # 最大推理批大小
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inference_timeout_ms: int = 5000 # 单次推理超时
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enable_fp16: bool = True # FP16半精度推理
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model_cache_size_gb: float = 4.0 # 模型内存缓存大小
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@dataclass
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class GPUConfig:
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"""GPU/NPU硬件加速配置"""
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device: str = "cuda" # 推理设备: cuda / cpu / npu
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gpu_ids: List[int] = field(default_factory=lambda: [0]) # 使用的GPU编号
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gpu_memory_fraction: float = 0.8 # GPU显存使用比例上限
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enable_tensorrt: bool = True # 是否启用TensorRT加速
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tensorrt_precision: str = "fp16" # TensorRT精度: fp32/fp16/int8
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triton_url: str = "localhost:8001" # Triton Inference Server地址
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@dataclass
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class CeleryConfig:
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"""Celery任务队列配置"""
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broker_url: str = "redis://localhost:6379/0" # Redis Broker地址
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result_backend: str = "redis://localhost:6379/1" # 结果存储后端
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task_serializer: str = "json"
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result_serializer: str = "json"
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task_default_queue: str = "writech.default"
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task_time_limit: int = 300 # 任务最大执行时间（秒）
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task_soft_time_limit: int = 240 # 软超时（触发SoftTimeLimitExceeded）
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worker_concurrency: int = 8 # Worker并发数
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worker_prefetch_multiplier: int = 2 # 预取倍数
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@dataclass
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class DatabaseConfig:
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"""数据库配置"""
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mysql_url: str = "mysql+pymysql://user:password@localhost:3306/writech_ai"
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redis_url: str = "redis://localhost:6379/0"
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mongodb_url: str = "mongodb://localhost:27017/writech_stroke"
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pool_size: int = 20 # 连接池大小
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pool_recycle: int = 3600 # 连接回收时间（秒）
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@dataclass
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class LogConfig:
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"""日志配置"""
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level: str = "INFO"
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format: str = "%(asctime)s [%(levelname)s] %(name)s: %(message)s"
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log_dir: str = "/var/log/writech-ai"
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max_file_size_mb: int = 100 # 单个日志文件大小上限
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backup_count: int = 10 # 保留日志文件数量
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enable_audit_log: bool = True # 启用审计日志
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audit_log_file: str = "audit.log" # 审计日志文件名
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# ==================== 安全配置 ====================
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@dataclass
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class SecurityConfig:
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"""安全配置"""
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# mTLS双向认证（安全设计：内部服务间mTLS双向认证）
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enable_mtls: bool = True
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server_cert_path: str = "/etc/ssl/server.crt"
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server_key_path: str = "/etc/ssl/server.key"
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ca_cert_path: str = "/etc/ssl/ca.crt"
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# 模型文件加密（安全设计：模型文件加密存储，推理时内存解密）
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model_encryption_enabled: bool = True
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model_encryption_key_env: str = "WRITECH_MODEL_KEY" # 加密密钥从环境变量读取
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# 请求校验（安全设计：输入数据格式校验与大小限制）
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max_stroke_points: int = 100000 # 单次请求最大坐标点数
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max_strokes_per_request: int = 500 # 单次请求最大笔画数
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max_text_length: int = 10000 # 作文文本最大长度
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# 速率限制
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rate_limit_per_minute: int = 600 # 每分钟最大请求数
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rate_limit_burst: int = 50 # 突发请求数
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# 审计日志（安全设计：所有识别请求记录调用方、时间、模型版本）
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enable_audit: bool = True
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audit_retention_days: int = 90 # 审计日志保留天数
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# ==================== mTLS认证管理 ====================
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class MTLSAuthenticator:
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"""
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mTLS双向认证管理器
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验证客户端证书，确保只有授权的内部服务可以调用AI引擎
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"""
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def __init__(self, config: SecurityConfig):
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self._config = config
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self._trusted_clients: Dict[str, str] = {} # 授信客户端证书指纹
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logger.info("mTLS认证管理器初始化")
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def load_certificates(self) -> bool:
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"""加载服务端证书和CA证书"""
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try:
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cert_path = Path(self._config.server_cert_path)
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key_path = Path(self._config.server_key_path)
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ca_path = Path(self._config.ca_cert_path)
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if not cert_path.exists():
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logger.warning(f"服务端证书不存在: {cert_path}")
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return False
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logger.info("mTLS证书加载完成")
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return True
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except Exception as e:
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logger.error(f"证书加载失败: {str(e)}")
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return False
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def verify_client_cert(self, cert_fingerprint: str) -> bool:
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"""验证客户端证书指纹"""
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if not self._config.enable_mtls:
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return True
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is_trusted = cert_fingerprint in self._trusted_clients
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if not is_trusted:
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logger.warning(f"未授信的客户端证书: {cert_fingerprint}")
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return is_trusted
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def register_trusted_client(self, name: str, fingerprint: str):
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"""注册授信客户端"""
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self._trusted_clients[fingerprint] = name
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logger.info(f"注册授信客户端: {name}")
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# ==================== 请求签名校验 ====================
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class RequestValidator:
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"""
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请求签名校验器
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对API请求进行HMAC签名校验，防止请求篡改和重放攻击
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"""
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def __init__(self, secret_key: str = ""):
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self._secret = secret_key or os.environ.get("WRITECH_API_SECRET", "default-secret")
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self._nonce_cache: Dict[str, float] = {} # 随机数缓存（防重放）
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self._nonce_ttl = 300 # 随机数有效期（秒）
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def generate_signature(self, payload: str, timestamp: int, nonce: str) -> str:
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"""生成请求签名"""
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message = f"{payload}×tamp={timestamp}&nonce={nonce}"
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return hmac.new(
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self._secret.encode(), message.encode(), hashlib.sha256
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).hexdigest()
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def verify_signature(self, payload: str, timestamp: int,
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nonce: str, signature: str) -> bool:
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"""
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校验请求签名
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1. 检查时间戳是否在有效窗口内（防重放）
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2. 检查随机数是否已使用（防重放）
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3. 验证HMAC签名是否匹配（防篡改）
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"""
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# 时间窗口校验（±5分钟）
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current_time = int(time.time())
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if abs(current_time - timestamp) > 300:
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logger.warning(f"请求时间戳过期: {timestamp}")
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return False
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# 随机数防重放检查
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if nonce in self._nonce_cache:
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logger.warning(f"重复的请求随机数: {nonce}")
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return False
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# HMAC签名验证
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expected = self.generate_signature(payload, timestamp, nonce)
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is_valid = hmac.compare_digest(expected, signature)
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if is_valid:
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# 缓存随机数
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self._nonce_cache[nonce] = time.time()
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self._cleanup_nonce_cache()
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return is_valid
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def _cleanup_nonce_cache(self):
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"""清理过期的随机数缓存"""
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current = time.time()
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expired = [k for k, v in self._nonce_cache.items() if current - v > self._nonce_ttl]
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for k in expired:
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del self._nonce_cache[k]
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# ==================== 全局配置管理器 ====================
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class Settings:
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"""
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全局配置管理器（单例）
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从环境变量和配置文件加载配置，支持运行时热更新
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环境变量优先级高于配置文件
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"""
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_instance = None
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def __new__(cls):
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if cls._instance is None:
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cls._instance = super().__new__(cls)
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return cls._instance
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def __init__(self):
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if hasattr(self, '_initialized'):
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return
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self._initialized = True
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# 加载各模块配置
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self.server = ServerConfig()
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self.model = ModelConfig()
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self.gpu = GPUConfig()
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self.celery = CeleryConfig()
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self.database = DatabaseConfig()
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self.log = LogConfig()
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self.security = SecurityConfig()
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# 从环境变量覆盖配置
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self._load_from_env()
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# 初始化安全组件
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self.mtls_auth = MTLSAuthenticator(self.security)
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self.request_validator = RequestValidator()
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logger.info("全局配置加载完成")
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def _load_from_env(self):
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"""从环境变量加载配置（覆盖默认值）"""
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env_mapping = {
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"WRITECH_HTTP_PORT": ("server", "http_port", int),
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"WRITECH_GRPC_PORT": ("server", "grpc_port", int),
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"WRITECH_WORKERS": ("server", "workers", int),
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"WRITECH_DEBUG": ("server", "debug", lambda x: x.lower() == "true"),
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"WRITECH_MODELS_DIR": ("model", "models_dir", str),
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"WRITECH_GPU_DEVICE": ("gpu", "device", str),
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"WRITECH_GPU_IDS": ("gpu", "gpu_ids", lambda x: [int(i) for i in x.split(",")]),
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"WRITECH_REDIS_URL": ("celery", "broker_url", str),
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"WRITECH_MYSQL_URL": ("database", "mysql_url", str),
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"WRITECH_LOG_LEVEL": ("log", "level", str),
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"WRITECH_ENABLE_MTLS": ("security", "enable_mtls", lambda x: x.lower() == "true"),
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}
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for env_key, (section, field, converter) in env_mapping.items():
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value = os.environ.get(env_key)
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if value is not None:
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config_obj = getattr(self, section)
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try:
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setattr(config_obj, field, converter(value))
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logger.info(f"环境变量覆盖配置: {env_key} -> {section}.{field}")
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except (ValueError, TypeError) as e:
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logger.warning(f"环境变量转换失败: {env_key}={value}, 错误: {str(e)}")
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def load_from_file(self, config_path: str):
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"""从JSON配置文件加载配置"""
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try:
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with open(config_path, 'r') as f:
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config_data = json.load(f)
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logger.info(f"配置文件加载完成: {config_path}")
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# 逐section更新配置
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for section_name, section_data in config_data.items():
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if hasattr(self, section_name) and isinstance(section_data, dict):
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config_obj = getattr(self, section_name)
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for key, value in section_data.items():
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if hasattr(config_obj, key):
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setattr(config_obj, key, value)
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except FileNotFoundError:
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logger.warning(f"配置文件不存在: {config_path}")
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except json.JSONDecodeError as e:
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logger.error(f"配置文件JSON解析错误: {str(e)}")
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def to_dict(self) -> Dict[str, Any]:
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"""将所有配置导出为字典（隐藏敏感信息）"""
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result = {}
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for section in ['server', 'model', 'gpu', 'celery', 'log']:
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config_obj = getattr(self, section)
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section_dict = {}
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for key in vars(config_obj):
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value = getattr(config_obj, key)
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# 隐藏密码和密钥类字段
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if any(kw in key.lower() for kw in ['password', 'secret', 'key', 'token']):
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section_dict[key] = "***"
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else:
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section_dict[key] = value
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result[section] = section_dict
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return result
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# 全局配置实例
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settings = Settings()
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