{"id":97704,"date":"2026-08-30T02:16:54","date_gmt":"2026-08-29T18:16:54","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/97704.html"},"modified":"2026-08-30T02:16:54","modified_gmt":"2026-08-29T18:16:54","slug":"%e6%b7%b1%e5%85%a5%e6%b5%85%e5%87%batinyml-13%ef%bc%9a%e5%8e%9f%e5%a7%8b%e6%b3%a2%e5%bd%a2%e5%92%8c%e4%ba%ba%e5%b7%a5%e7%89%b9%e5%be%81%e5%ba%94%e8%af%a5%e5%a6%82%e4%bd%95%e9%80%89%e6%8b%a9%ef%bc%9f","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/97704.html","title":{"rendered":"\u6df1\u5165\u6d45\u51faTinyML 13\uff1a\u539f\u59cb\u6ce2\u5f62\u548c\u4eba\u5de5\u7279\u5f81\u5e94\u8be5\u5982\u4f55\u9009\u62e9\uff1f"},"content":{"rendered":"

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    \"\"<\/p>\n

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    Python：\u4e3a\u540c\u4e00\u7a97\u53e3\u540c\u65f6\u751f\u6210\u7b80\u5355\u65f6\u57df\u7279\u5f81<\/p>\n\n\n\n
    \n

    import numpy as np \u00a0 def time_features(window): \u00a0\u00a0\u00a0\u00a0# window\u5f62\u72b6: [time, channels] \u00a0\u00a0\u00a0\u00a0return np.concatenate([ \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0window.mean(axis=0), \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0window.std(axis=0), \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0np.sqrt(np.mean(window ** 2, axis=0)), \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0np.ptp(window, axis=0), \u00a0\u00a0\u00a0\u00a0]).astype(np.float32) \u00a0 # \u4e0e\u539f\u59cb\u6ce2\u5f62\u6a21\u578b\u4f7f\u7528\u540c\u4e00\u6570\u636e\u5212\u5206\u8fdb\u884c\u5bf9\u7167\u3002<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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    \u7528\u76f8\u540c\u5212\u5206\u5efa\u7acb\u53ef\u5f52\u56e0\u7684\u5bf9\u7167\u5b9e\u9a8c<\/h3>\n

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    \u7b2c\u4e00\u6761\u57fa\u7ebf\u53ef\u4ee5\u662f\u5c11\u91cf\u65f6\u57df\u7279\u5f81\u52a0\u7ebf\u6027\u5206\u7c7b\u5668，\u7528\u4e8e\u5224\u65ad\u4efb\u52a1\u662f\u5426\u7b80\u5355；\u7b2c\u4e8c\u6761\u662f\u9891\u5e26\u80fd\u91cf\u52a0\u5c0f\u578bDense；\u7b2c\u4e09\u6761\u662f\u539f\u59cb\u7a97\u53e3\u52a0\u8f7b\u91cf1D CNN\u3002\u53ea\u8981\u7b80\u5355\u57fa\u7ebf\u8fbe\u5230\u4e1a\u52a1\u76ee\u6807，\u5c31\u6ca1\u6709\u5fc5\u8981\u4e3a\u4e86\u7ed3\u6784\u590d\u6742\u800c\u589e\u52a0\u90e8\u7f72\u98ce\u9669\u3002<\/p>\n

    \u9519\u8bef\u5206\u6790\u8981\u5bfb\u627e\u8868\u793a\u7f3a\u5931\u3002\u82e5\u4e24\u4e2a\u7c7b\u522bRMS\u76f8\u8fd1\u4f46\u9891\u7387\u4e0d\u540c，\u65f6\u57df\u80fd\u91cf\u7279\u5f81\u96be\u4ee5\u533a\u5206；\u82e5\u6545\u969c\u662f\u77ed\u4fc3\u51b2\u51fb，\u6574\u7a97\u5e73\u5747\u9891\u8c31\u53ef\u80fd\u7a00\u91ca\u4e8b\u4ef6\u3002\u6b64\u65f6\u66f4\u6362\u8868\u793a\u6bd4\u76f2\u76ee\u589e\u52a0\u795e\u7ecf\u5143\u66f4\u6709\u9488\u5bf9\u6027\u3002<\/p>\n\n\n\n
    \u7279\u5f81\u5206\u79bb\u5ea6\u7684\u76f4\u89c2\u6307\u6807： score = |mean_class_A – mean_class_B| \/ (std_A + std_B) \u5b83\u53ea\u7528\u4e8e\u521d\u7b5b\u5355\u4e2a\u7279\u5f81，\u4e0d\u80fd\u66ff\u4ee3\u72ec\u7acb\u6d4b\u8bd5\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u56db\u3001\u843d\u5730\u68c0\u67e5<\/h3>\n
  • \u4e09\u79cd\u8f93\u5165\u8def\u7ebf\u4f7f\u7528\u540c\u4e00\u539f\u59cb\u6570\u636e\u548c\u540c\u4e00\u5206\u7ec4\u5212\u5206\u3002<\/li>\n
  • \u628a\u7279\u5f81\u8ba1\u7b97\u65f6\u95f4\u3001\u4e34\u65f6\u7f13\u51b2\u548c\u5e38\u91cf\u8868\u8ba1\u5165\u8d44\u6e90\u3002<\/li>\n
  • \u901a\u8fc7\u5206\u5e03\u56fe\u548c\u6d88\u878d\u5b9e\u9a8c\u786e\u8ba4\u6bcf\u4e2a\u7279\u5f81\u7684\u8d21\u732e\u3002<\/li>\n
  • \u8bad\u7ec3\u7aef\u4e0eMCU\u7aef\u4f7f\u7528\u9ec4\u91d1\u7a97\u53e3\u9010\u9879\u6bd4\u8f83\u3002<\/li>\n
  • \u9009\u62e9\u8fbe\u5230\u9a8c\u6536\u6307\u6807\u4e14\u603b\u7cfb\u7edf\u6210\u672c\u6700\u4f4e\u7684\u8f93\u5165\u65b9\u6848\u3002<\/li>\n

    \u52a8\u624b：\u6bd4\u8f83\u539f\u59cb\u6ce2\u5f62\u4e0e\u56db\u4e2a\u65f6\u57df\u7279\u5f81<\/h3>\n

    \u811a\u672c\u751f\u6210\u4e24\u7c7b\u4fe1\u53f7：\u4e00\u7c7b\u4f4e\u5e45\u5e73\u7a33\u632f\u52a8，\u4e00\u7c7b\u5305\u542b\u66f4\u9ad8\u5e45\u503c\u548c\u5076\u53d1\u51b2\u51fb\u3002\u5b83\u63d0\u53d6\u5747\u503c\u3001RMS\u3001\u5cf0\u5cf0\u503c\u548c\u8fc7\u96f6\u7387，\u518d\u8ba1\u7b97\u6bcf\u4e2a\u7279\u5f81\u7684\u7b80\u5355\u5206\u79bb\u5ea6\u3002\u6240\u6709\u6570\u636e\u7531\u56fa\u5b9a\u79cd\u5b50\u751f\u6210\u3002<\/p>\n

    \u5b9e\u9a8c\u73af\u5883\u4e0e\u8f93\u5165<\/h4>\n
      \n
    • Python 3\u6807\u51c6\u5e93\u3002<\/li>\n
    • \u4fdd\u5b58\u4e3a `compare_representations.py` \u5e76\u8fd0\u884c\u3002<\/li>\n
    • \u6a21\u62df\u4fe1\u53f7\u7528\u4e8e\u5b66\u4e60\u65b9\u6cd5，\u4e0d\u4f5c\u4e3a\u771f\u5b9e\u8bbe\u5907\u6027\u80fd\u8bc1\u636e\u3002<\/li>\n<\/ul>\n

      \u6309\u987a\u5e8f\u5b8c\u6210\u5b9e\u9a8c<\/h4>\n
    • \u8fd0\u884c\u811a\u672c，\u89c2\u5bdf\u6bcf\u7c7b\u539f\u59cb\u7a97\u53e3\u6709100\u4e2a\u5143\u7d20，\u800c\u7279\u5f81\u5411\u91cf\u53ea\u67094\u4e2a\u5143\u7d20\u3002<\/li>\n
    • \u6bd4\u8f83\u56db\u4e2a\u7279\u5f81\u7684\u5206\u79bb\u5ea6，\u627e\u51fa\u5bf9\u5f53\u524d\u6a21\u62df\u4efb\u52a1\u6700\u6709\u6548\u7684\u7279\u5f81\u3002<\/li>\n
    • \u5220\u9664\u6545\u969c\u7c7b\u51b2\u51fb，\u53ea\u4fdd\u7559\u9891\u7387\u5dee\u5f02，\u518d\u8fd0\u884c\u5e76\u89c2\u5bdf\u54ea\u4e9b\u65f6\u57df\u7279\u5f81\u53d8\u5f31\u3002<\/li>\n
    • \u589e\u52a0\u4e00\u4e2a\u9891\u7387\u76f8\u5173\u7279\u5f81\u6216\u9891\u5e26\u80fd\u91cf，\u9a8c\u8bc1\u8868\u793a\u5e94\u968f\u4efb\u52a1\u8bc1\u636e\u53d8\u5316\u3002<\/li>\n

      \u53ef\u76f4\u63a5\u8fd0\u884c：\u4ece\u6a21\u62df\u7a97\u53e3\u63d0\u53d6\u7279\u5f81\u5e76\u8ba1\u7b97\u5206\u79bb\u5ea6<\/p>\n\n\n\n
      \n

      import math import random import statistics \u00a0 random.seed(13) FS, N = 100, 100 \u00a0 def make_window(label, index): \u00a0\u00a0\u00a0\u00a0values = [] \u00a0\u00a0\u00a0\u00a0for n in range(N): \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0t = n \/ FS \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0amp = 0.20 if label == 0 else 0.42 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0value = amp * math.sin(2*math.pi*(5+label*2)*t) \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0value += random.gauss(0, 0.025) \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0if label == 1 and n == 20 + index % 50: \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0value += 1.1 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0values.append(value) \u00a0\u00a0\u00a0\u00a0return values \u00a0 def features(x): \u00a0\u00a0\u00a0\u00a0mean = sum(x) \/ len(x) \u00a0\u00a0\u00a0\u00a0rms = math.sqrt(sum(v*v for v in x) \/ len(x)) \u00a0\u00a0\u00a0\u00a0p2p = max(x) – min(x) \u00a0\u00a0\u00a0\u00a0zcr = sum((a >= 0) != (b >= 0) for a, b in zip(x, x[1:]))\/(len(x)-1) \u00a0\u00a0\u00a0\u00a0return [mean, rms, p2p, zcr] \u00a0 groups = {0: [], 1: []} for label in groups: \u00a0\u00a0\u00a0\u00a0groups[label] = [features(make_window(label, i)) for i in range(30)] \u00a0 names = ["mean", "rms", "peak_to_peak", "zero_cross_rate"] print("raw elements\/window:", N, "feature elements\/window:", len(names)) for j, name in enumerate(names): \u00a0\u00a0\u00a0\u00a0a = [row[j] for row in groups[0]] \u00a0\u00a0\u00a0\u00a0b = [row[j] for row in groups[1]] \u00a0\u00a0\u00a0\u00a0score = abs(statistics.mean(a)-statistics.mean(b)) \/ (statistics.pstdev(a)+statistics.pstdev(b)+1e-12) \u00a0\u00a0\u00a0\u00a0print(f"{name:16s} mean0={statistics.mean(a):.4f} " \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0f"mean1={statistics.mean(b):.4f} separation={score:.3f}")<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u5148\u8bfb\u61c2\u4ee3\u7801\u4e2d\u7684\u5173\u952e\u8def\u5f84<\/h4>\n
    • \u6a21\u62df\u7c7b\u5dee\u5f02\u540c\u65f6\u5305\u542b\u5e45\u503c\u3001\u9891\u7387\u548c\u51b2\u51fb，\u4fbf\u4e8e\u89c2\u5bdf\u4e0d\u540c\u7279\u5f81\u6355\u83b7\u4e0d\u540c\u8bc1\u636e\u3002<\/li>\n
    • features\u53ea\u7528\u5f53\u524d\u7a97\u53e3\u8ba1\u7b97，\u4e0d\u8bfb\u53d6\u6807\u7b7e，\u9632\u6b62\u628a\u7b54\u6848\u6cc4\u6f0f\u8fdb\u8f93\u5165\u3002<\/li>\n
    • \u5206\u79bb\u5ea6\u628a\u7c7b\u95f4\u5747\u503c\u5dee\u9664\u4ee5\u7c7b\u5185\u6ce2\u52a8，\u53ea\u505a\u5355\u7279\u5f81\u521d\u7b5b，\u4e0d\u4ee3\u8868\u975e\u7ebf\u6027\u6a21\u578b\u6548\u679c\u3002<\/li>\n
    • \u6240\u6709\u8def\u7ebf\u5fc5\u987b\u590d\u7528\u540c\u4e00\u7a97\u53e3ID，\u5426\u5219\u8868\u793a\u6bd4\u8f83\u4f1a\u6df7\u5165\u6570\u636e\u5212\u5206\u5dee\u5f02\u3002<\/li>\n

      \u4f60\u5e94\u8be5\u89c2\u5bdf\u5230\u4ec0\u4e48<\/h4>\n
        \n
      • \u7279\u5f81\u5411\u91cf\u4ece100\u4e2a\u5143\u7d20\u538b\u7f29\u52304\u4e2a\u5143\u7d20，\u76f4\u63a5\u51cf\u5c11\u540e\u7eedDense\u5c42\u8f93\u5165\u3002<\/li>\n
      • RMS\u6216\u5cf0\u5cf0\u503c\u5bf9\u5f53\u524d\u5e45\u503c\u4e0e\u51b2\u51fb\u5dee\u5f02\u901a\u5e38\u5177\u6709\u8f83\u9ad8\u5206\u79bb\u5ea6\u3002<\/li>\n
      • \u5220\u9664\u51b2\u51fb\u6216\u4fee\u6539\u7c7b\u522b\u5dee\u5f02\u540e，\u6700\u6709\u6548\u7279\u5f81\u4f1a\u53d8\u5316，\u8bc1\u660e\u7279\u5f81\u9009\u62e9\u4f9d\u8d56\u4efb\u52a1\u3002<\/li>\n<\/ul>\n

        \u6210\u529f\u6807\u51c6<\/h4>\n
      • \u4e09\u6761\u8868\u793a\u8def\u7ebf\u4f7f\u7528\u5b8c\u5168\u76f8\u540c\u7684\u539f\u59cb\u6837\u672cID\u548c\u96c6\u5408\u5212\u5206\u3002<\/li>\n
      • \u9884\u5904\u7406\u8017\u65f6\u3001\u6a21\u578b\u8017\u65f6\u548cRAM\u4e00\u8d77\u8fdb\u5165\u6bd4\u8f83\u3002<\/li>\n
      • \u6700\u7ec8\u9009\u62e9\u7531\u72ec\u7acb\u6d4b\u8bd5\u3001\u8d44\u6e90\u548c\u9519\u8bef\u7c7b\u578b\u5171\u540c\u652f\u6301\u3002<\/li>\n

        \u5931\u8d25\u65f6\u4ece\u54ea\u91cc\u67e5\u8d77<\/h4>\n

        \u8f93\u5165\u8868\u793a\u5bf9\u7167\u5b9e\u9a8c\u5e38\u89c1\u95ee\u9898<\/p>\n\n\u73b0\u8c61\u539f\u56e0\u5904\u7406<\/tr>\n\n\n\n\n
        \u8bad\u7ec3\u5f88\u597d\u3001\u6362\u8bbe\u5907\u5f88\u5dee<\/td>\n\u7279\u5f81\u5305\u542b\u8bbe\u5907\u504f\u7f6e<\/td>\n\u6309\u8bbe\u5907\u9694\u79bb\u5e76\u68c0\u67e5\u5747\u503c\u7c7b\u7279\u5f81<\/td>\n<\/tr>\n
        \u7279\u5f81\u6a21\u578b\u5b8c\u5168\u5206\u4e0d\u5f00<\/td>\n\u6458\u8981\u4e22\u5931\u5173\u952e\u65f6\u5e8f<\/td>\n\u52a0\u5165\u9891\u57df\u6216\u539f\u59cb\u6ce2\u5f62\u8def\u7ebf<\/td>\n<\/tr>\n
        \u7279\u5f81\u8def\u7ebf\u677f\u7aef\u66f4\u6162<\/td>\n\u6f0f\u7b97FFT\u6216\u9664\u6cd5\u6210\u672c<\/td>\n\u7aef\u5230\u7aef\u6d4b\u9884\u5904\u7406\u52a0\u63a8\u7406<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        \u7279\u5f81\u5de5\u7a0b\u7684\u76ee\u6807\u662f\u7528\u66f4\u4f4e\u8d44\u6e90\u4fdd\u7559\u4efb\u52a1\u8bc1\u636e\u3002\u6bcf\u6b21\u6dfb\u52a0\u7279\u5f81\u90fd\u5e94\u8bf4\u660e\u5b83\u63cf\u8ff0\u4ec0\u4e48\u7269\u7406\u5c5e\u6027\u3002<\/p>\n

        \u628a\u5b9e\u9a8c\u8fc1\u79fb\u5230\u771f\u5b9eMCU\u9879\u76ee<\/h4>\n

        \u628a\u7279\u5f81\u51fd\u6570\u5148\u7528\u5e38\u91cf\u3001\u6b63\u5f26\u3001\u5355\u8109\u51b2\u505a\u5355\u5143\u6d4b\u8bd5，\u518d\u79fb\u690dC\u3002\u8ba1\u7b97\u8017\u65f6\u5fc5\u987b\u5305\u542b\u6ee4\u6ce2\u3001\u5e73\u65b9\u6839\u3001\u9664\u6cd5\u548c\u8de8\u901a\u9053\u64cd\u4f5c\u3002<\/p>\n

        \u82e5\u9009\u62e9\u539f\u59cb\u6ce2\u5f62，\u4ecd\u9700\u505a\u5355\u4f4d\u6362\u7b97\u548c\u6700\u5c0f\u8d28\u91cf\u68c0\u67e5；\u201c\u7aef\u5230\u7aef\u5b66\u4e60\u201d\u4e0d\u610f\u5473\u7740\u56fa\u4ef6\u53ef\u4ee5\u5ffd\u7565\u91c7\u6837\u7387\u3001\u901a\u9053\u548c\u9971\u548c\u3002<\/p>\n

        \u628a\u7ed3\u679c\u518d\u5411\u524d\u63a8\u8fdb\u4e00\u6b65<\/h3>\n
      • \u4e3a\u4f60\u7684\u4efb\u52a1\u5199\u51fa\u4e09\u4e2a\u53ef\u80fd\u6709\u533a\u5206\u529b\u7684\u7269\u7406\u5c5e\u6027，\u518d\u6620\u5c04\u5230\u7279\u5f81\u3002<\/li>\n
      • \u8ba1\u7b97600\u7ef4\u539f\u59cb\u8f93\u5165\u4e0e24\u7ef4\u7279\u5f81\u8f93\u5165\u8fde\u63a516\u4e2a\u795e\u7ecf\u5143\u65f6\u7684\u53c2\u6570\u91cf\u5dee\u5f02\u3002<\/li>\n
      • \u5217\u51fa\u539f\u59cb\u3001\u65f6\u57df\u3001\u9891\u57df\u4e09\u6761\u57fa\u7ebf\u7684\u7edf\u4e00\u8bc4\u4ef7\u8868\u5b57\u6bb5\u3002<\/li>\n

        \u6536\u675f：\u539f\u59cb\u6ce2\u5f62\u4e0e\u4eba\u5de5\u7279\u5f81\u6ca1\u6709\u56fa\u5b9a\u80dc\u8005\u3002\u7528\u540c\u4e00\u6570\u636e\u3001\u540c\u4e00\u5212\u5206\u548c\u5b8c\u6574\u677f\u7aef\u8d44\u6e90\u8d26\u5355\u6bd4\u8f83，\u624d\u80fd\u9009\u51fa\u9002\u5408\u9879\u76ee\u7684\u8f93\u5165\u65b9\u6848\u3002<\/p>\n

        \u53c2\u8003\u8d44\u6599： Arm CMSIS-DSP \u5b98\u65b9\u6587\u6863 TensorFlow Lite Micro \u5b98\u65b9\u4ee3\u7801\u4ed3\u5e93<\/p>\n","protected":false},"excerpt":{"rendered":"

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