wangsli
11a2d4a4b9
- 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
150 行
4.2 KiB
JSON
150 行
4.2 KiB
JSON
[
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{
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"name": "预处理算子",
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"version": "1.1.0",
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"author": "Antigravity",
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"description": "去直流、带通滤波（默认10-400Hz）",
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"type": "python",
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"module": "src.sample_algorithms",
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"function": "dc_removal_and_bandpass",
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"inputs": {
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"raw_data": {
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"field": "raw_data",
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"type": "ndarray",
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"description": "原始声呐数据"
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},
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"fs": {
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"field": "sample_rate",
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"type": "float",
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"description": "采样率"
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}
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},
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"outputs": {
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"clean_data": {
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"index": 0,
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"type": "ndarray",
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"description": "预处理后数据"
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}
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}
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},
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{
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"name": "时频分析",
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"version": "1.1.0",
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"author": "Antigravity",
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"description": "STFT 幅度谱计算",
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"type": "python",
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"module": "src.sample_algorithms",
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"function": "stft_magnitude",
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"inputs": {
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"clean_data": {
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"field": "clean_data",
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"type": "ndarray",
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"description": "清洗后数据"
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}
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},
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"outputs": {
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"stft_data": {
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"index": 0,
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"type": "ndarray",
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"description": "STFT矩阵"
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}
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}
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},
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{
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"name": "SNR估计",
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"version": "1.1.0",
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"author": "Antigravity",
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"description": "基于 RMS/Var 的简易信噪比估计",
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"type": "python",
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"module": "src.sample_algorithms",
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"function": "snr_estimation",
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"inputs": {
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"raw_data": {
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"field": "raw_data",
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"type": "ndarray",
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"description": "待评估数据"
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}
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},
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"outputs": {
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"snr_value": {
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"index": 0,
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"type": "float",
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"description": "信噪比(dB)"
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}
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}
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},
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{
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"name": "质量巡检",
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"version": "1.1.0",
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"author": "Antigravity",
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"description": "掉点、饱和与数据完整性检测",
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"type": "python",
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"module": "src.sample_algorithms",
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"function": "quality_check",
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"inputs": {
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"raw_data": {
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"field": "raw_data",
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"type": "ndarray",
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"description": "待校验原始数据"
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}
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},
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"outputs": {
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"core_index": {
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"index": 0,
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"type": "dict",
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"description": "质量指标集"
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}
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}
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},
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{
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"name": "C++ 增益算子",
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"version": "1.0.0",
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"author": "Antigravity",
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"description": "使用 C++ 侧实现的高效增益处理(1.5x)",
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"type": "cpp",
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"lib_path": "src/sample_algos_cpp/simple_ops.so",
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"function": "multiply_by_factor_cpp",
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"inputs": {
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"in_data": {
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"field": "clean_data",
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"type": "ndarray",
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"description": "输入数据"
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}
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},
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"outputs": {
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"gain_data": {
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"type": "ndarray",
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"index": 0,
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"description": "增益后数据"
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}
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}
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},
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{
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"name": "MATLAB 带通算子",
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"version": "1.0.0",
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"author": "Antigravity",
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"description": "基于 MATLAB .m 实现的带通滤波",
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"type": "matlab",
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"m_path": "src/sample_algos_matlab",
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"function": "bandpass_filter_m",
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"inputs": {
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"in_data": {
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"field": "raw_data",
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"type": "ndarray",
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"description": "输入数据"
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},
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"fs": {
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"field": "sample_rate",
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"type": "float",
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"description": "采样率"
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}
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},
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"outputs": {
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"m_filtered": {
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"type": "ndarray",
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"index": 0,
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"description": "滤波后数据"
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}
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}
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}
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] |