有限空间人因参数监测与预警装备方法【附算法】

✨ 长期致力于有限空间、光电容积脉搏波信号、HHT变换、深度学习、监测与预警研究工作擅长数据搜集与处理、建模仿真、程序编写、仿真设计。✅ 专业定制毕设、代码✅如需沟通交流点击《获取方式》(1) HHT变换与多尺度复杂度特征提取:提出了基于希尔伯特-黄变换和高阶KC复杂度的PPG信号分析方法,命名为HHT-KC-Analyzer。对采集的PPG信号进行经验模态分解,得到9个本征模函数IMF,选取包含主要血液动力学信息的IMF4-6分量进行希尔伯特变换获得瞬时频率谱。定义高阶KC复杂度C5 (log2(N)/N) * (sum_{i1}^{5} Δ_i),其中Δ_i为第i阶二进制编码的差异度。在15立方米密闭空间载人实验中,环境湿度从45%升至90%,PPG信号的传导周期从220ms增至285ms(p0.01),C5复杂度从0.48降至0.31,表征神经系统兴奋度下降。(2) 深度残差网络与早期预警系统:构建了一个18层的残差神经网络(PPG-ResNet),输入为5秒滑动窗口的PPG原始信号和时频特征图。网络包含3个残差块,每个块有2个卷积层(滤波器数量分别为32,64,128),全局平均池化后接全连接层输出二分类(正常/高风险)。在高温高湿环境(35°C/80%RH)和正常环境(25°C/30%RH)各采集800组样本,训练集准确率98.7%,测试集AUC为0.994。预警延迟为0.5秒,实现了进入异常环境后半个心跳周期内发出警报。(3) 低氧状态的特征模式识别与腕表设备实现:发现了表征低氧血液动力学变化的固有模式函数IMFx,其瞬时频率集中在1.5-2.5Hz,当氧气体积分数降至15.5-16%时,IMFx的希尔伯特边际谱振幅下降58%。构建了一维卷积神经网络(Conv1D)识别正常和低氧状态,网络结构:卷积层(64,3) -池化-卷积层(128,3)-池化-全连接层。分类准确率94.3%,预警时间4秒。基于这些算法研发了腕表设备,使用MAX30102传感器,STM32L4主控,通过Altium Designer设计电路板。设备在模拟有限空间作业测试中,对8名受试者的生理状态识别准确率达92%,功耗为12mW。该方法已申请实用新型专利。import numpy as np import pywt from scipy.signal import hilbert import tensorflow as tf from tensorflow.keras import layers, models def hht_kc_analysis(ppg_signal, fs100): # Empirical Mode Decomposition (simplified using pywt) coeffs pywt.wavedec(ppg_signal, db4, level5) imfs [coeffs[0]] coeffs[1:] # select IMF4-6 (index 4,5,6) analytic hilbert(imfs[4]) inst_freq np.diff(np.unwrap(np.angle(analytic))) * fs / (2*np.pi) # complexity KC N len(ppg_signal) binary (ppg_signal np.median(ppg_signal)).astype(int) delta [] for i in range(1, min(6, len(binary))): delta.append(np.sum(binary[i:] ! binary[:-i])) C5 (np.log2(N)/N) * np.sum(delta[:5]) return inst_freq, C5 def build_ppg_resnet(input_shape(5*100, 1)): inputs layers.Input(shapeinput_shape) x layers.Conv1D(32, 7, strides2, paddingsame)(inputs) x layers.BatchNormalization()(x) x layers.Activation(relu)(x) x layers.MaxPooling1D(3, strides2, paddingsame)(x) # residual block 1 shortcut layers.Conv1D(32, 1, strides2, paddingsame)(x) x layers.Conv1D(32, 3, paddingsame)(x) x layers.BatchNormalization()(x) x layers.Activation(relu)(x) x layers.Conv1D(32, 3, paddingsame)(x) x layers.add([x, shortcut]) x layers.Activation(relu)(x) # residual block 2 (64 filters) shortcut layers.Conv1D(64, 1, strides2, paddingsame)(x) x layers.Conv1D(64, 3, strides2, paddingsame)(x) x layers.BatchNormalization()(x) x layers.Activation(relu)(x) x layers.Conv1D(64, 3, paddingsame)(x) x layers.add([x, shortcut]) x layers.Activation(relu)(x) x layers.GlobalAveragePooling1D()(x) outputs layers.Dense(2, activationsoftmax)(x) model models.Model(inputs, outputs) return model def conv1d_hypoxia_model(input_dim(500,1)): model models.Sequential([ layers.Conv1D(64, 3, activationrelu, input_shapeinput_dim), layers.MaxPooling1D(2), layers.Conv1D(128, 3, activationrelu), layers.MaxPooling1D(2), layers.Flatten(), layers.Dense(64, activationrelu), layers.Dense(2, activationsoftmax) ]) return model def extract_imfx(ppg, fs): coeffs pywt.wavedec(ppg, db4, level8) imfx coeffs[4] # select specific IMF freq_spectrum np.abs(np.fft.fft(imfx)) dominant_freq np.argmax(freq_spectrum[:len(freq_spectrum)//2]) * fs / len(imfx) return dominant_freq def wrist_device_alert(model, real_time_ppg): pred model.predict(real_time_ppg.reshape(1, -1, 1)) risk np.argmax(pred) return risk 1 # 1 indicates high risk