feat: v3.5 - Multi-language acoustic algorithm scheduler
- Core: Python/C++/MATLAB tri-protocol bridge architecture - Validation: 5-type real-time field, type, dimension, completeness, and compatibility checking - UI: Premium Deep Sea theme with visual pipeline preview - Modules: Sea Trial Exclusive + Exception Hub + Algorithm Management - Tests: Full coverage with 5 automated test cases
这个提交包含在：
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from abc import ABC, abstractmethod
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from src.models import WaterSoundData
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import os
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import shutil
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import tempfile
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from typing import Callable, Dict, List, Any, Optional
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class AlgorithmBridge(ABC):
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"""桥接插件层抽象基类"""
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def __init__(self, name: str, is_core: bool = False):
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self.name = name
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self.is_core = is_core # 核心算法标志，用于容错策略
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@abstractmethod
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def call_algo(self, water_data: WaterSoundData) -> WaterSoundData:
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pass
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class PythonBridge(AlgorithmBridge):
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"""Python 桥接插件"""
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def __init__(self, name: str, algo_func: Callable,
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input_mapping: Dict[str, str],
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output_mapping: Dict[str, str],
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preprocessing: Optional[List[Dict[str, Any]]] = None,
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is_core: bool = False,
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requirements: Optional[Dict[str, Any]] = None,
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output_specs: Optional[Dict[str, Any]] = None,
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algo_type: str = "python"):
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super().__init__(name, is_core)
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self.algo_func = algo_func # Python算法核心函数
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self.input_mapping = input_mapping # 输入映射 {algo_param: water_data_attr}
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self.output_mapping = output_mapping# 输出映射 {algo_return_index: water_data_attr}
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self.preprocessing = preprocessing or [] # 预处理算子 [{ "op": "norm", "params": {...} }]
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self.requirements = requirements or {}
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self.output_specs = output_specs or {}
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self.algo_type = algo_type
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def _apply_preprocessing(self, data: Any, ops: List[Dict[str, Any]]) -> Any:
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"""应用轻量级预处理算子"""
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import numpy as np
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for op_config in ops:
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op = op_config.get("op")
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params = op_config.get("params", {})
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if op == "normalize" and isinstance(data, np.ndarray):
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# 简单归一化示例
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if data.size == 0:
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continue
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min_val = params.get("min", 0)
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max_val = params.get("max", 1)
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data_min = float(data.min())
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data_max = float(data.max())
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denom = data_max - data_min
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if denom == 0:
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data = np.full_like(data, float(min_val), dtype=float)
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else:
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data = (data.astype(float) - data_min) / denom * (float(max_val) - float(min_val)) + float(min_val)
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elif op == "scale" and isinstance(data, (int, float, np.ndarray)):
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data = data * params.get("factor", 1.0)
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return data
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def call_algo(self, water_data: WaterSoundData) -> WaterSoundData:
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try:
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water_data.add_log(self.name, "Start Execution")
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# 1. 提取并映射输入，同时应用预处理
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algo_inputs = {}
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for param, attr in self.input_mapping.items():
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if hasattr(water_data, attr):
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val = getattr(water_data, attr)
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# 查找是否有针对该字段的预处理
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relevant_ops = [op for op in self.preprocessing if op.get("field") == attr]
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if relevant_ops:
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val = self._apply_preprocessing(val, relevant_ops)
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algo_inputs[param] = val
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else:
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raise AttributeError(f"Field {attr} not found in WaterSoundData")
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# 2. 调用算法
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results = self.algo_func(**algo_inputs)
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# 3. 映射结果回 standardization 数据
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if not isinstance(results, (list, tuple)):
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results = [results]
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for key, attr in self.output_mapping.items():
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# key can be an index (int) or a key if results is dict
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if isinstance(results, dict):
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val = results[key]
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else:
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idx = int(key) if isinstance(key, str) and key.isdigit() else key
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val = results[idx]
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setattr(water_data, attr, val)
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water_data.add_log(self.name, "Finished Execution")
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except Exception as e:
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water_data.set_error(f"Algorithm {self.name} failed: {str(e)}")
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return water_data
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import ctypes
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import numpy as np
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class CppBridge(AlgorithmBridge):
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"""C++ 桥接插件 (ctypes 实现)"""
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def __init__(self, name: str, lib_path: str, function_name: str,
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input_mapping: Dict[str, str],
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output_mapping: Dict[str, str],
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preprocessing: Optional[List[Dict[str, Any]]] = None,
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requirements: Optional[Dict[str, Any]] = None,
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output_specs: Optional[Dict[str, Any]] = None,
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is_core: bool = False):
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super().__init__(name, is_core)
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self.lib_path = lib_path
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self.function_name = function_name
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self.input_mapping = input_mapping
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self.output_mapping = output_mapping
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self.preprocessing = preprocessing or []
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self.requirements = requirements or {}
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self.output_specs = output_specs or {}
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self.algo_type = "cpp"
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self._lib = None
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self._func = None
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def _load_lib(self):
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"""延迟加载动态库"""
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if self._lib is None:
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if not os.path.exists(self.lib_path):
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raise FileNotFoundError(f"C++ library not found: {self.lib_path}")
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self._lib = ctypes.CDLL(self.lib_path)
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self._func = getattr(self._lib, self.function_name)
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# 注意：在 C 侧我们需要定义统一签名，例如:
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# void func(double* in, int in_len, double* out, int out_len)
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def call_algo(self, water_data: WaterSoundData) -> WaterSoundData:
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try:
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water_data.add_log(self.name, "Start C++ Execution")
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self._load_lib()
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# 1. 准备输入
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# 为了简化 Phase 3.3，我们假设目前只支持 1进1出 (double array)
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# 实际生产中会根据 input_mapping 构造更复杂的结构
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in_attr = list(self.input_mapping.values())[0]
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in_data = getattr(water_data, in_attr)
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if not isinstance(in_data, np.ndarray):
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raise TypeError(f"C++ bridge expected numpy array for {in_attr}")
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# 确保数据连续且为 float64 (double)
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in_data = np.ascontiguousarray(in_data, dtype=np.float64)
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in_ptr = in_data.ctypes.data_as(ctypes.POINTER(ctypes.c_double))
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in_len = ctypes.c_int(in_data.size)
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# 2. 准备输出空间
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# 同样简化为与输入等长
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out_data = np.zeros_like(in_data)
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out_ptr = out_data.ctypes.data_as(ctypes.POINTER(ctypes.c_double))
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out_len = ctypes.c_int(out_data.size)
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# 3. 调用 C++ 函数
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# 默认签名: void (double*, int, double*, int)
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self._func(in_ptr, in_len, out_ptr, out_len)
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# 4. 映射结果回 standardization 数据
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out_attr = list(self.output_mapping.values())[0]
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setattr(water_data, out_attr, out_data)
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water_data.add_log(self.name, "Finished C++ Execution")
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except Exception as e:
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water_data.set_error(f"C++ Algorithm {self.name} failed: {str(e)}")
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return water_data
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class MatlabBridge(AlgorithmBridge):
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"""MATLAB/Octave 桥接插件 (支持 Engine/Oct2py/File 降级)"""
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def __init__(self, name: str, m_path: str, function_name: str,
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input_mapping: Dict[str, str],
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output_mapping: Dict[str, str],
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preprocessing: Optional[List[Dict[str, Any]]] = None,
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requirements: Optional[Dict[str, Any]] = None,
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output_specs: Optional[Dict[str, Any]] = None,
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is_core: bool = False):
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super().__init__(name, is_core)
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self.m_path = m_path # .m 文件所在目录
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self.function_name = function_name
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self.input_mapping = input_mapping
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self.output_mapping = output_mapping
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self.preprocessing = preprocessing or []
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self.requirements = requirements or {}
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self.output_specs = output_specs or {}
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self.algo_type = "matlab"
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self._engine = None
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def _call_via_engine(self, inputs: Dict[str, Any]) -> Any:
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"""方式 1: 使用官方 matlab.engine"""
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import matlab.engine
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if self._engine is None:
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self._engine = matlab.engine.start_matlab()
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self._engine.addpath(self.m_path)
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func = getattr(self._engine, self.function_name)
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# 将 numpy 数组转为 matlab 类型 (这里简化处理)
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m_inputs = [matlab.double(v.tolist()) if isinstance(v, np.ndarray) else v for v in inputs.values()]
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return func(*m_inputs, nargout=len(self.output_mapping))
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def _call_via_oct2py(self, inputs: Dict[str, Any]) -> Any:
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"""方式 2: 使用开源 oct2py (Octave)"""
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from oct2py import octave
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octave.addpath(self.m_path)
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func = getattr(octave, self.function_name)
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return func(*inputs.values())
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def _call_via_file(self, inputs: Dict[str, Any]) -> Any:
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"""方式 3: 文件兜底 (使用 .mat 文件交换)"""
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from scipy.io import savemat, loadmat
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import subprocess
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with tempfile.TemporaryDirectory(prefix="matlab_bridge_") as tmp_dir:
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in_file = os.path.join(tmp_dir, "tmp_in.mat")
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out_file = os.path.join(tmp_dir, "tmp_out.mat")
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savemat(in_file, inputs)
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# 构造执行脚本：加入 m_path，加载输入，调用函数，保存输出
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m_path = os.path.abspath(self.m_path).replace("'", "''")
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in_file_escaped = in_file.replace("'", "''")
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out_file_escaped = out_file.replace("'", "''")
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arglist = ",".join(inputs.keys())
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script = (
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f"addpath('{m_path}'); "
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f"load('{in_file_escaped}'); "
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f"results = {self.function_name}({arglist}); "
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f"save('-mat', '{out_file_escaped}', 'results'); "
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"exit;"
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)
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octave = shutil.which("octave") or shutil.which("octave-cli")
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matlab = shutil.which("matlab")
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if octave:
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cmd = [octave, "--quiet", "--eval", script]
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elif matlab:
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cmd = [matlab, "-batch", script]
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else:
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raise RuntimeError("No MATLAB/Octave CLI found for file-based execution.")
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subprocess.run(cmd, check=True, capture_output=True, text=True)
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res = loadmat(out_file)
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return res.get("results")
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def call_algo(self, water_data: WaterSoundData) -> WaterSoundData:
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try:
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water_data.add_log(self.name, "Start MATLAB Execution")
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# 准备输入
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algo_inputs = {}
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for param, attr in self.input_mapping.items():
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algo_inputs[param] = getattr(water_data, attr)
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# 自动寻优/降级逻辑
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from src.env_detect import collect_env_info
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env = collect_env_info()
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if env["matlab"]["engine_available"]:
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results = self._call_via_engine(algo_inputs)
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elif env["matlab"]["oct2py_available"]:
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results = self._call_via_oct2py(algo_inputs)
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elif env["matlab"]["octave_cli"]:
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results = self._call_via_file(algo_inputs)
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else:
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raise RuntimeError("No MATLAB/Octave environment found for execution.")
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# 结果回写 (支持单出/多出)
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if not isinstance(results, (list, tuple)):
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results = [results]
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for key, attr in self.output_mapping.items():
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idx = int(key) if key.isdigit() else 0
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setattr(water_data, attr, results[idx])
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water_data.add_log(self.name, "Finished MATLAB Execution")
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except Exception as e:
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water_data.set_error(f"MATLAB Algorithm {self.name} failed: {str(e)}")
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return water_data
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