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# \u4f7f\u7528Kubernetes\u9032\u884c\u96f2\u908a\u8cc7\u6e90\u7de8\u6392\r\napiVersion: v1\r\nkind: Pod\r\nmetadata:\r\n name: edge-node\r\nspec:\r\n containers:\r\n - name: data-processor\r\n image: data-processor:v1\r\n resources:\r\n limits:\r\n cpu: 100m\r\n memory: 128Mi\r\n nodeSelector:\r\n location: edge\r\n<\/code><\/pre>\n\u7da0\u8272\u7bc0\u80fd:\u53ef\u6301\u7e8c\u767c\u5c55\u7684\u5fc5\u7531\u4e4b\u8def<\/strong><\/h2>\n
\/\/ \u4f7f\u7528Python\u76e3\u63a7\u4f3a\u670d\u5668CPU\u6eab\u5ea6\r\nimport psutil\r\n\r\ndef get_cpu_temp():\r\n temp = psutil.sensors_temperatures()\r\n if not temp:\r\n return None\r\n \r\n for name, entries in temp.items():\r\n if name.startswith('coretemp'):\r\n return entries[0].current\r\n \r\nwhile True:\r\n cpu_temp = get_cpu_temp()\r\n print(f\"CPU\u6eab\u5ea6: {cpu_temp}\u00b0C\")\r\n \r\n if cpu_temp > 80:\r\n # \u89f8\u767c\u544a\u8b66\u8207\u964d\u983b\u63aa\u65bd \r\n send_alert(f\"CPU\u6eab\u5ea6\u904e\u9ad8: {cpu_temp}\u00b0C\")\r\n underclock_cpu()\r\n \r\n time.sleep(60) # \u6bcf\u5206\u9418\u6aa2\u67e5\u4e00\u6b21\r\n<\/code><\/pre>\nAI\u8ce6\u80fd:\u667a\u6167\u904b\u7dad\u7684\u672a\u4f86<\/strong><\/h2>\n
# \u4f7f\u7528TensorFlow\u9032\u884c\u4f3a\u670d\u5668\u8ca0\u8f09\u9810\u6e2c \r\nimport tensorflow as tf\r\n\r\nmodel = tf.keras.Sequential([\r\n tf.keras.layers.LSTM(128, input_shape=(30, 5)), \r\n tf.keras.layers.Dense(64, activation='relu'),\r\n tf.keras.layers.Dense(1) \r\n])\r\n\r\nmodel.compile(optimizer='adam', loss='mse')\r\n\r\n# \u8a13\u7df4\u8cc7\u6599: \u904e\u53bb30\u5929\u7684CPU\u3001\u8a18\u61b6\u9ad4\u3001I\/O\u7b49\u6307\u6a19\r\ntrain_data = [...]\r\n\r\nmodel.fit(train_data, epochs=100, batch_size=32) \r\n\r\n# \u9810\u6e2c\u672a\u4f8624\u5c0f\u6642\u7684\u8ca0\u8f09\u8da8\u52e2\r\nfuture_load = model.predict(recent_metrics) \r\n<\/code><\/pre>\n