diff --git a/.idea/encodings.xml b/.idea/encodings.xml
new file mode 100644
index 0000000..c96602a
--- /dev/null
+++ b/.idea/encodings.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Encoding">
+    <file url="file://$PROJECT_DIR$/FC_ML_Baseline/FC_ML_Baseline_Test/Data_Handle/training.log" charset="UTF-8" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/FC_ML_Baseline/FC_ML_Baseline_Test/Data_Handle/forecast.json b/FC_ML_Baseline/FC_ML_Baseline_Test/Data_Handle/forecast.json
new file mode 100644
index 0000000..9f64f01
--- /dev/null
+++ b/FC_ML_Baseline/FC_ML_Baseline_Test/Data_Handle/forecast.json
@@ -0,0 +1 @@
+{"label1": -33.618247985839844, "label2": 0.769456148147583, "label3": 13.0462007522583, "label4": 4.485231399536133, "label5": -30.332216262817383, "label6": -30.264156341552734, "label7": 2.244447708129883, "label8": -15.965958595275879}
\ No newline at end of file
diff --git a/FC_ML_Baseline/FC_ML_Baseline_Test/Data_Handle/model.bin b/FC_ML_Baseline/FC_ML_Baseline_Test/Data_Handle/model.bin
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index 0000000..4719ad0
Binary files /dev/null and b/FC_ML_Baseline/FC_ML_Baseline_Test/Data_Handle/model.bin differ
diff --git a/FC_ML_Baseline/FC_ML_Baseline_Test/Data_Handle/model.pth b/FC_ML_Baseline/FC_ML_Baseline_Test/Data_Handle/model.pth
new file mode 100644
index 0000000..f3f25fe
Binary files /dev/null and b/FC_ML_Baseline/FC_ML_Baseline_Test/Data_Handle/model.pth differ
diff --git a/FC_ML_Baseline/FC_ML_Baseline_Test/Data_Handle/training.log b/FC_ML_Baseline/FC_ML_Baseline_Test/Data_Handle/training.log
new file mode 100644
index 0000000..4ce5177
--- /dev/null
+++ b/FC_ML_Baseline/FC_ML_Baseline_Test/Data_Handle/training.log
@@ -0,0 +1,272 @@
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+Epoch 270 | Train Loss: 0.0624 | Test Loss: 0.1331 | 损失比: 0.47:1
+Epoch 280 | Train Loss: 0.0623 | Test Loss: 0.1328 | 损失比: 0.47:1
+Epoch 290 | Train Loss: 0.0623 | Test Loss: 0.1324 | 损失比: 0.47:1
+Epoch 0 | Train Loss: 0.3257 | Test Loss: 0.2338 | 损失比: 1.39:1
+Epoch 10 | Train Loss: 0.1292 | Test Loss: 0.1531 | 损失比: 0.84:1
+Epoch 20 | Train Loss: 0.1048 | Test Loss: 0.1526 | 损失比: 0.69:1
+Epoch 30 | Train Loss: 0.0880 | Test Loss: 0.1500 | 损失比: 0.59:1
+Epoch 40 | Train Loss: 0.0775 | Test Loss: 0.1463 | 损失比: 0.53:1
+Epoch 50 | Train Loss: 0.0715 | Test Loss: 0.1437 | 损失比: 0.50:1
+Epoch 60 | Train Loss: 0.0683 | Test Loss: 0.1424 | 损失比: 0.48:1
+Epoch 70 | Train Loss: 0.0664 | Test Loss: 0.1409 | 损失比: 0.47:1
+Epoch 80 | Train Loss: 0.0653 | Test Loss: 0.1392 | 损失比: 0.47:1
+Epoch 90 | Train Loss: 0.0645 | Test Loss: 0.1381 | 损失比: 0.47:1
+Epoch 100 | Train Loss: 0.0640 | Test Loss: 0.1369 | 损失比: 0.47:1
+Epoch 110 | Train Loss: 0.0637 | Test Loss: 0.1361 | 损失比: 0.47:1
+Epoch 120 | Train Loss: 0.0634 | Test Loss: 0.1355 | 损失比: 0.47:1
+Epoch 130 | Train Loss: 0.0632 | Test Loss: 0.1352 | 损失比: 0.47:1
+Epoch 140 | Train Loss: 0.0630 | Test Loss: 0.1342 | 损失比: 0.47:1
+Epoch 150 | Train Loss: 0.0629 | Test Loss: 0.1338 | 损失比: 0.47:1
+Epoch 160 | Train Loss: 0.0628 | Test Loss: 0.1334 | 损失比: 0.47:1
+Epoch 170 | Train Loss: 0.0627 | Test Loss: 0.1330 | 损失比: 0.47:1
+Epoch 180 | Train Loss: 0.0626 | Test Loss: 0.1322 | 损失比: 0.47:1
+Epoch 190 | Train Loss: 0.0625 | Test Loss: 0.1319 | 损失比: 0.47:1
+Epoch 200 | Train Loss: 0.0624 | Test Loss: 0.1315 | 损失比: 0.47:1
+Epoch 210 | Train Loss: 0.0624 | Test Loss: 0.1310 | 损失比: 0.48:1
+Epoch 220 | Train Loss: 0.0623 | Test Loss: 0.1310 | 损失比: 0.48:1
+Epoch 230 | Train Loss: 0.0623 | Test Loss: 0.1303 | 损失比: 0.48:1
+Epoch 240 | Train Loss: 0.0622 | Test Loss: 0.1301 | 损失比: 0.48:1
+Epoch 250 | Train Loss: 0.0622 | Test Loss: 0.1300 | 损失比: 0.48:1
+Epoch 260 | Train Loss: 0.0621 | Test Loss: 0.1298 | 损失比: 0.48:1
+Epoch 270 | Train Loss: 0.0621 | Test Loss: 0.1301 | 损失比: 0.48:1
+Epoch 280 | Train Loss: 0.0621 | Test Loss: 0.1296 | 损失比: 0.48:1
+Epoch 290 | Train Loss: 0.0620 | Test Loss: 0.1297 | 损失比: 0.48:1
+Epoch 0 | Train Loss: 0.3614 | Test Loss: 0.2802 | Loss Factor: 1.29:1
+Epoch 10 | Train Loss: 0.1415 | Test Loss: 0.1612 | Loss Factor: 0.88:1
+Epoch 20 | Train Loss: 0.1115 | Test Loss: 0.1352 | Loss Factor: 0.82:1
+Epoch 30 | Train Loss: 0.0916 | Test Loss: 0.1180 | Loss Factor: 0.78:1
+Epoch 40 | Train Loss: 0.0795 | Test Loss: 0.1090 | Loss Factor: 0.73:1
+Epoch 50 | Train Loss: 0.0722 | Test Loss: 0.1074 | Loss Factor: 0.67:1
+Epoch 60 | Train Loss: 0.0687 | Test Loss: 0.1098 | Loss Factor: 0.63:1
+Epoch 70 | Train Loss: 0.0667 | Test Loss: 0.1129 | Loss Factor: 0.59:1
+Epoch 80 | Train Loss: 0.0655 | Test Loss: 0.1164 | Loss Factor: 0.56:1
+Epoch 90 | Train Loss: 0.0646 | Test Loss: 0.1188 | Loss Factor: 0.54:1
+Epoch 100 | Train Loss: 0.0641 | Test Loss: 0.1212 | Loss Factor: 0.53:1
+Epoch 110 | Train Loss: 0.0637 | Test Loss: 0.1227 | Loss Factor: 0.52:1
+Epoch 120 | Train Loss: 0.0635 | Test Loss: 0.1237 | Loss Factor: 0.51:1
+Epoch 130 | Train Loss: 0.0633 | Test Loss: 0.1245 | Loss Factor: 0.51:1
+Epoch 140 | Train Loss: 0.0631 | Test Loss: 0.1249 | Loss Factor: 0.51:1
+Epoch 150 | Train Loss: 0.0630 | Test Loss: 0.1255 | Loss Factor: 0.50:1
+Epoch 160 | Train Loss: 0.0629 | Test Loss: 0.1254 | Loss Factor: 0.50:1
+Epoch 170 | Train Loss: 0.0628 | Test Loss: 0.1259 | Loss Factor: 0.50:1
+Epoch 180 | Train Loss: 0.0627 | Test Loss: 0.1258 | Loss Factor: 0.50:1
+Epoch 190 | Train Loss: 0.0627 | Test Loss: 0.1260 | Loss Factor: 0.50:1
+Epoch 200 | Train Loss: 0.0626 | Test Loss: 0.1263 | Loss Factor: 0.50:1
+Epoch 210 | Train Loss: 0.0625 | Test Loss: 0.1262 | Loss Factor: 0.50:1
+Epoch 220 | Train Loss: 0.0625 | Test Loss: 0.1262 | Loss Factor: 0.50:1
+Epoch 230 | Train Loss: 0.0624 | Test Loss: 0.1268 | Loss Factor: 0.49:1
+Epoch 240 | Train Loss: 0.0624 | Test Loss: 0.1267 | Loss Factor: 0.49:1
+Epoch 250 | Train Loss: 0.0623 | Test Loss: 0.1268 | Loss Factor: 0.49:1
+Epoch 260 | Train Loss: 0.0623 | Test Loss: 0.1268 | Loss Factor: 0.49:1
+Epoch 270 | Train Loss: 0.0623 | Test Loss: 0.1269 | Loss Factor: 0.49:1
+Epoch 280 | Train Loss: 0.0622 | Test Loss: 0.1271 | Loss Factor: 0.49:1
+Epoch 290 | Train Loss: 0.0622 | Test Loss: 0.1271 | Loss Factor: 0.49:1