{"id":76957,"date":"2026-02-23T17:05:42","date_gmt":"2026-02-23T09:05:42","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/76957.html"},"modified":"2026-02-23T17:05:42","modified_gmt":"2026-02-23T09:05:42","slug":"2025%e6%97%b6%e9%97%b4%e5%ba%8f%e5%88%97%e9%a1%b6%e4%bc%9a%e8%ae%ba%e6%96%87%e7%b2%be%e9%80%89%ef%bc%9a%e4%bb%8e%e7%90%86%e8%ae%ba%e5%88%b0%e5%ae%9e%e8%b7%b5%e7%9a%84%e4%bb%a3%e7%a0%81%e5%ae%9e","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/76957.html","title":{"rendered":"2025\u65f6\u95f4\u5e8f\u5217\u9876\u4f1a\u8bba\u6587\u7cbe\u9009\uff1a\u4ece\u7406\u8bba\u5230\u5b9e\u8df5\u7684\u4ee3\u7801\u5b9e\u73b0\u6307\u5357"},"content":{"rendered":"

1. \u65f6\u95f4\u5e8f\u5217\u5206\u6790\u7684\u524d\u6cbf\u8d8b\u52bf\u4e0e2025\u9876\u4f1a\u4eae\u70b9<\/h3>\n

2025\u5e74\u7684\u9876\u4f1a\u8bba\u6587\u5728\u65f6\u95f4\u5e8f\u5217\u9886\u57df\u5448\u73b0\u51fa\u51e0\u4e2a\u660e\u663e\u8d8b\u52bf\u3002\u9996\u5148\u662f\u591a\u6a21\u6001\u878d\u5408\u6210\u4e3a\u4e3b\u6d41，\u50cfTime-VLM\u8fd9\u6837\u7684\u5de5\u4f5c\u5f00\u59cb\u5c1d\u8bd5\u5c06\u89c6\u89c9\u3001\u6587\u672c\u7b49\u6a21\u6001\u4fe1\u606f\u4e0e\u4f20\u7edf\u65f6\u5e8f\u6570\u636e\u7ed3\u5408\u3002\u6211\u5728\u590d\u73b0\u8fd9\u7c7b\u6a21\u578b\u65f6\u53d1\u73b0，\u5173\u952e\u5728\u4e8e\u5982\u4f55\u8bbe\u8ba1\u6709\u6548\u7684\u7279\u5f81\u4ea4\u53c9\u6a21\u5757\u2014\u2014\u76f4\u63a5\u62fc\u63a5\u4e0d\u540c\u6a21\u6001\u7684\u7279\u5f81\u5f80\u5f80\u6548\u679c\u4e0d\u4f73，\u9700\u8981\u5f15\u5165\u6ce8\u610f\u529b\u673a\u5236\u6216\u4ea4\u53c9\u7f51\u7edc\u8fdb\u884c\u7cbe\u7ec6\u878d\u5408\u3002<\/p>\n

\u53e6\u4e00\u4e2a\u91cd\u8981\u65b9\u5411\u662f\u6548\u7387\u4f18\u5316\u3002\u968f\u7740\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u91cf\u6fc0\u589e，\u4f20\u7edfTransformer\u67b6\u6784\u7684\u8ba1\u7b97\u5f00\u9500\u6210\u4e3a\u74f6\u9888\u3002CASA\u8bba\u6587\u63d0\u51fa\u7684CNN\u81ea\u7f16\u7801\u5668\u66ff\u4ee3\u65b9\u6848\u786e\u5b9e\u4ee4\u4eba\u773c\u524d\u4e00\u4eae，\u5b9e\u6d4b\u4e0b\u6765\u5728\u957f\u5e8f\u5217\u4efb\u52a1\u4e0a\u5185\u5b58\u5360\u7528\u51cf\u5c11\u4e86\u8fd180%，\u800c\u7cbe\u5ea6\u635f\u5931\u4e0d\u52302%\u3002\u8fd9\u91cc\u6709\u4e2a\u5c0f\u6280\u5de7：\u5728\u5b9e\u73b0\u65f6\u53ef\u4ee5\u5148\u5bf9\u8f93\u5165\u5e8f\u5217\u505a\u5206\u6bb5\u5904\u7406，\u518d\u9001\u5165\u7f16\u7801\u5668，\u80fd\u8fdb\u4e00\u6b65\u964d\u4f4e\u8ba1\u7b97\u590d\u6742\u5ea6\u3002<\/p>\n

\u5f02\u5e38\u68c0\u6d4b\u9886\u57df\u4e5f\u51fa\u73b0\u4e86\u7a81\u7834\u6027\u8fdb\u5c55\u3002AERCA\u6a21\u578b\u901a\u8fc7\u5f15\u5165\u5916\u751f\u53d8\u91cf\u5efa\u6a21，\u89e3\u51b3\u4e86\u4f20\u7edf\u65b9\u6cd5\u5728\u590d\u6742\u56e0\u679c\u573a\u666f\u4e0b\u7684\u5c40\u9650\u6027\u3002\u6211\u5efa\u8bae\u5728\u590d\u73b0\u65f6\u91cd\u70b9\u5173\u6ce8\u5b83\u7684\u72ec\u7acb\u6027\u7ea6\u675f\u5b9e\u73b0\u2014\u2014\u8fd9\u4e2a\u7ec6\u8282\u5bf9\u6700\u7ec8\u6548\u679c\u5f71\u54cd\u5f88\u5927，\u4f46\u539f\u8bba\u6587\u7684\u4ee3\u7801\u4e2d\u5e76\u6ca1\u6709\u8be6\u7ec6\u6ce8\u91ca\u3002<\/p>\n

2. \u6838\u5fc3\u8bba\u6587\u4ee3\u7801\u89e3\u6790\u4e0e\u590d\u73b0\u6307\u5357<\/h3>\n

2.1 Time-VLM\u7684\u591a\u6a21\u6001\u5b9e\u73b0\u8981\u70b9<\/h4>\n

\u8fd9\u4e2a\u6a21\u578b\u7684\u4ee3\u7801\u7ed3\u6784\u4e3b\u8981\u5305\u542b\u4e09\u4e2a\u6838\u5fc3\u6a21\u5757：<\/p>\n

  • \u65f6\u5e8f\u7279\u5f81\u63d0\u53d6\u5668（1D CNN + GRU）<\/li>\n
  • \u89c6\u89c9\u8f6c\u6362\u5668（\u5c06\u65f6\u5e8f\u6570\u636e\u8f6c\u4e3a\u683c\u62c9\u59c6\u89d2\u573a\u56fe\u50cf）<\/li>\n
  • \u6587\u672c\u63d0\u793a\u751f\u6210\u5668（\u57fa\u4e8e\u6a21\u677f\u7684\u6587\u672c\u751f\u6210）<\/li>\n

    \u590d\u73b0\u65f6\u6700\u5bb9\u6613\u51fa\u95ee\u9898\u7684\u662f\u56fe\u50cf\u751f\u6210\u73af\u8282\u3002\u539f\u8bba\u6587\u4f7f\u7528\u683c\u62c9\u59c6\u89d2\u573a（Gramian Angular Field）\u8f6c\u6362，\u4f46\u9ed8\u8ba4\u53c2\u6570\u5bf9\u9ad8\u9891\u566a\u58f0\u654f\u611f\u3002\u6211\u7684\u7ecf\u9a8c\u662f\u52a0\u5165\u6ed1\u52a8\u5e73\u5747\u9884\u5904\u7406：<\/p>\n

    def smooth_series(x, window_size=5):
    \n return np.convolve(x, np.ones(window_size)\/window_size, mode='same')<\/p>\n

    # \u751f\u6210\u56fe\u50cf\u524d\u5148\u5e73\u6ed1
    \nsmoothed = smooth_series(raw_ts)
    \ngadf = GramianAngularField().transform(smoothed.reshape(1,-1))<\/p>\n

    2.2 CASA\u7684\u9ad8\u6548\u6ce8\u610f\u529b\u66ff\u4ee3\u65b9\u6848<\/h4>\n

    CASA\u7684\u6838\u5fc3\u521b\u65b0\u5728\u4e8e\u7528\u5377\u79ef\u5757\u66ff\u4ee3\u6807\u51c6\u6ce8\u610f\u529b\u3002\u5b83\u7684\u5b9e\u73b0\u6709\u4e2a\u5de7\u5999\u4e4b\u5904：\u4f7f\u7528\u6269\u5f20\u5377\u79ef（dilated convolution）\u6765\u6269\u5927\u611f\u53d7\u91ce\u3002\u5728PyTorch\u4e2d\u7684\u5173\u952e\u5b9e\u73b0\u5982\u4e0b：<\/p>\n

    class CASAEncoder(nn.Module):
    \n def __init__(self, channels, dilation_rates=[1,3,5]):
    \n super().__init__()
    \n self.convs = nn.ModuleList([
    \n nn.Conv1d(channels, channels, 3, padding=d, dilation=d)
    \n for d in dilation_rates
    \n ])<\/p>\n

    def forward(self, x):
    \n attn_scores = 0
    \n for conv in self.convs:
    \n attn_scores = attn_scores + conv(x)
    \n return attn_scores \/ len(self.convs)<\/p>\n

    \u5b9e\u6d4b\u53d1\u73b0，\u5f53\u5e8f\u5217\u957f\u5ea6\u8d85\u8fc7512\u65f6，\u8fd9\u79cd\u8bbe\u8ba1\u7684\u4f18\u52bf\u4f1a\u975e\u5e38\u660e\u663e\u3002\u4e0d\u8fc7\u8981\u6ce8\u610f\u68af\u5ea6\u7206\u70b8\u95ee\u9898\u2014\u2014\u5efa\u8bae\u521d\u59cb\u5316\u65f6\u628a\u5377\u79ef\u6838\u6743\u91cd\u8c03\u5c0f（std=0.02\u6548\u679c\u4e0d\u9519）\u3002<\/p>\n

    3. \u5b9e\u6218\u4e2d\u7684\u5e38\u89c1\u95ee\u9898\u4e0e\u89e3\u51b3\u65b9\u6848<\/h3>\n

    3.1 \u6570\u636e\u9884\u5904\u7406\u7684\u5751<\/h4>\n

    \u5f88\u591a\u8bba\u6587\u5728\u6570\u636e\u6807\u51c6\u5316\u73af\u8282\u8bed\u7109\u4e0d\u8be6，\u4f46\u8fd9\u5bf9\u6a21\u578b\u6548\u679c\u5f71\u54cd\u5de8\u5927\u3002\u7279\u522b\u662f\u5904\u7406\u91d1\u878d\u3001\u533b\u7597\u6570\u636e\u65f6，\u6211\u5f3a\u70c8\u63a8\u8350\u4f7f\u7528RobustScaler\u800c\u4e0d\u662f\u5e38\u89c4\u7684MinMaxScaler：<\/p>\n

    from sklearn.preprocessing import RobustScaler<\/p>\n

    # \u6bd4StandardScaler\u5bf9\u5f02\u5e38\u503c\u66f4\u9c81\u68d2
    \nscaler = RobustScaler(
    \n quantile_range=(5, 95), # \u5ffd\u7565\u6781\u7aef\u503c
    \n with_scaling=True,
    \n with_centering=True
    \n)
    \nscaled_data = scaler.fit_transform(raw_data)<\/p>\n

    \u53e6\u4e00\u4e2a\u5bb9\u6613\u5ffd\u89c6\u7684\u95ee\u9898\u662f\u7f3a\u5931\u503c\u5904\u7406\u3002\u5bf9\u4e8e\u4e0d\u89c4\u5219\u65f6\u95f4\u5e8f\u5217，\u7ebf\u6027\u63d2\u503c\u5f80\u5f80\u4e0d\u5982\u524d\u5411\u586b\u5145+\u63a9\u7801\u77e9\u9635\u7684\u65b9\u6848：<\/p>\n

    def fill_missing(x):
    \n # \u524d\u5411\u586b\u5145
    \n filled = pd.DataFrame(x).fillna(method='ffill').values
    \n # \u751f\u6210\u63a9\u7801\u77e9\u9635
    \n mask = (~np.isnan(x)).astype(float)
    \n return np.concatenate([filled, mask], axis=-1)<\/p>\n

    3.2 \u8bad\u7ec3\u6280\u5de7\u4e0e\u8c03\u53c2\u7ecf\u9a8c<\/h4>\n

    \u5728\u8bad\u7ec3\u65f6\u5e8f\u6a21\u578b\u65f6，\u5b66\u4e60\u7387\u8c03\u5ea6\u7b56\u7565\u6bd4\u60f3\u8c61\u4e2d\u91cd\u8981\u3002\u57fa\u4e8e\u9a8c\u8bc1\u635f\u5931\u7684\u52a8\u6001\u8c03\u6574\u6548\u679c\u6700\u597d：<\/p>\n

    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
    \n optimizer,
    \n mode='min',
    \n factor=0.5,
    \n patience=3,
    \n verbose=True
    \n)<\/p>\n

    # \u6bcf\u4e2aepoch\u540e\u8c03\u7528
    \nval_loss = validate(model)
    \nscheduler.step(val_loss)<\/p>\n

    \u5bf9\u4e8e\u957f\u671f\u9884\u6d4b\u4efb\u52a1，\u8bfe\u7a0b\u5b66\u4e60（curriculum learning）\u5f88\u6709\u6548\u2014\u2014\u5148\u8ba9\u6a21\u578b\u5b66\u4e60\u9884\u6d4b\u77ed\u671f\u672a\u6765（\u59821-3\u6b65），\u518d\u9010\u6b65\u589e\u52a0\u9884\u6d4b\u957f\u5ea6\u3002\u8fd9\u4e2a\u6280\u5de7\u8ba9\u6211\u7684\u6d4b\u8bd5\u6307\u6807\u63d0\u5347\u4e86\u7ea615%\u3002<\/p>\n

    4. \u524d\u6cbf\u6280\u672f\u7684\u5ef6\u4f38\u5e94\u7528<\/h3>\n

    4.1 \u91d1\u878d\u65f6\u5e8f\u9884\u6d4b\u7684\u7279\u6b8a\u5904\u7406<\/h4>\n

    \u91d1\u878d\u6570\u636e\u5177\u6709\u9ad8\u566a\u58f0\u3001\u975e\u5e73\u7a33\u7b49\u7279\u6027\u3002Amplifier\u8bba\u6587\u7684\u80fd\u91cf\u653e\u5927\u6280\u672f\u5728\u8fd9\u91cc\u7279\u522b\u6709\u7528，\u4f46\u9700\u8981\u8c03\u6574\u9891\u8c31\u7ffb\u8f6c\u7684\u9608\u503c：<\/p>\n

    def energy_amplify(spectrum, threshold=0.2):
    \n # \u627e\u51fa\u4f4e\u80fd\u91cf\u6210\u5206
    \n mask = (spectrum < threshold * spectrum.max())
    \n # \u7ffb\u8f6c\u5e76\u653e\u5927
    \n amplified = spectrum * (1 + mask * (1\/spectrum – 1))
    \n return amplified \/ amplified.max() # \u4fdd\u6301\u80fd\u91cf\u5b88\u6052<\/p>\n

    \u5728\u5b9e\u9645\u4ea4\u6613\u7b56\u7565\u4e2d，\u5efa\u8bae\u7ed3\u5408\u591a\u4e2a\u6a21\u578b\u7684\u9884\u6d4b\u7ed3\u679c\u3002\u6211\u7684\u7ecf\u9a8c\u7ec4\u5408\u662f：30% Amplifier + 40% Time-VLM + 30% \u4f20\u7edfARIMA，\u8fd9\u6837\u65e2\u80fd\u6355\u6349\u975e\u7ebf\u6027\u5173\u7cfb\u53c8\u4fdd\u6301\u7a33\u5b9a\u6027\u3002<\/p>\n

    4.2 \u533b\u7597\u5065\u5eb7\u6570\u636e\u7684\u6ce8\u610f\u4e8b\u9879<\/h4>\n

    \u5904\u7406ECG\u3001EEG\u7b49\u533b\u7597\u6570\u636e\u65f6，AERCA\u7684\u5f02\u5e38\u68c0\u6d4b\u6846\u67b6\u8868\u73b0\u51fa\u8272\u3002\u4f46\u8981\u6ce8\u610f\u533b\u7597\u6570\u636e\u7684\u9690\u79c1\u4fdd\u62a4\u95ee\u9898\u2014\u2014\u53ef\u4ee5\u5728\u6a21\u578b\u524d\u7aef\u6dfb\u52a0\u5dee\u5206\u9690\u79c1\u6a21\u5757：<\/p>\n

    from opacus import PrivacyEngine<\/p>\n

    privacy_engine = PrivacyEngine(
    \n model,
    \n sample_rate=0.01,
    \n noise_multiplier=1.0,
    \n max_grad_norm=1.0
    \n)
    \nprivacy_engine.attach(optimizer)<\/p>\n

    \u8fd9\u7c7b\u6570\u636e\u901a\u5e38\u91c7\u6837\u9891\u7387\u4e0d\u4e00，\u5efa\u8bae\u5148\u7edf\u4e00\u91cd\u91c7\u6837\u5230\u56fa\u5b9a\u9891\u7387\u3002\u4f7f\u7528PyArrow\u7684resample\u5de5\u5177\u6548\u7387\u5f88\u9ad8：<\/p>\n

    import pyarrow as pa<\/p>\n

    table = pa.Table.from_pandas(df)
    \nresampled = table.groupby(
    \n pa.compute.floor(
    \n pa.compute.divide(
    \n pa.compute.subtract(
    \n pa.compute.cast(table['timestamp'], 'int64'),
    \n start_time
    \n ),
    \n interval_ns
    \n )
    \n )
    \n).aggregate([('value', 'mean')])<\/p>\n","protected":false},"excerpt":{"rendered":"

    1. \u65f6\u95f4\u5e8f\u5217\u5206\u6790\u7684\u524d\u6cbf\u8d8b\u52bf\u4e0e2025\u9876\u4f1a\u4eae\u70b9
    \n2025\u5e74\u7684\u9876\u4f1a\u8bba\u6587\u5728\u65f6\u95f4\u5e8f\u5217\u9886\u57df\u5448\u73b0\u51fa\u51e0\u4e2a\u660e\u663e\u8d8b\u52bf\u3002\u9996\u5148\u662f\u591a\u6a21\u6001\u878d\u5408\u6210\u4e3a\u4e3b\u6d41，\u50cfTime-VLM\u8fd9\u6837\u7684\u5de5\u4f5c\u5f00\u59cb\u5c1d\u8bd5\u5c06\u89c6\u89c9\u3001\u6587\u672c\u7b49\u6a21\u6001\u4fe1\u606f\u4e0e\u4f20\u7edf\u65f6\u5e8f\u6570\u636e\u7ed3\u5408\u3002\u6211\u5728\u590d\u73b0\u8fd9\u7c7b\u6a21\u578b\u65f6\u53d1\u73b0，\u5173\u952e\u5728\u4e8e\u5982\u4f55\u8bbe\u8ba1\u6709\u6548\u7684\u7279\u5f81\u4ea4\u53c9\u6a21\u5757\u2014\u2014\u76f4\u63a5\u62fc\u63a5\u4e0d\u540c\u6a21\u6001\u7684\u7279\u5f81\u5f80\u5f80\u6548\u679c\u4e0d\u4f73，\u9700\u8981\u5f15\u5165\u6ce8\u610f\u529b\u673a\u5236\u6216\u4ea4\u53c9\u7f51\u7edc\u8fdb\u884c\u7cbe\u7ec6\u878d\u5408\u3002
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