diff --git a/app/service/design/service.py b/app/service/design/service.py
index 4846a2d..798b6f4 100644
--- a/app/service/design/service.py
+++ b/app/service/design/service.py
@@ -191,5 +191,5 @@ def update_base_size_priority(layers, size):
     new_height = 700
     # 更新坐标
     for info in layers:
-        info['position'] = (info['position'][0], info['position'][1] - min_x)
+        info['adaptive_position'] = (info['position'][0], info['position'][1] - min_x)
     return layers, (new_width, new_height)
diff --git a/app/service/design/utils/synthesis_item.py b/app/service/design/utils/synthesis_item.py
index 59bb1de..03df2d9 100644
--- a/app/service/design/utils/synthesis_item.py
+++ b/app/service/design/utils/synthesis_item.py
@@ -69,12 +69,12 @@ def synthesis(data, size, basic_info):
             if d['name'] == 'body':
                 # 创建一个新的宽高透明图像， 把模特贴上去获取mask
                 transparent_image = Image.new("RGBA", size, (0, 0, 0, 0))
-                transparent_image.paste(d['image'], (d['position'][1], d['position'][0]), d['image'])  # 此处可变数组会被paste篡改值，所以使用下标获取position
+                transparent_image.paste(d['image'], (d['adaptive_position'][1], d['adaptive_position'][0]), d['image'])  # 此处可变数组会被paste篡改值，所以使用下标获取position
                 body_mask = np.array(transparent_image.split()[3])
 
                 # 根据新的坐标获取新的肩点
-                left_shoulder = [x + y for x, y in zip(basic_info['body_point_test']['shoulder_left'], [d['position'][1], d['position'][0]])]
-                right_shoulder = [x + y for x, y in zip(basic_info['body_point_test']['shoulder_right'], [d['position'][1], d['position'][0]])]
+                left_shoulder = [x + y for x, y in zip(basic_info['body_point_test']['shoulder_left'], [d['adaptive_position'][1], d['adaptive_position'][0]])]
+                right_shoulder = [x + y for x, y in zip(basic_info['body_point_test']['shoulder_right'], [d['adaptive_position'][1], d['adaptive_position'][0]])]
                 body_mask[:min(left_shoulder[1], right_shoulder[1]), left_shoulder[0]:right_shoulder[0]] = 255
         _, binary_body_mask = cv2.threshold(body_mask, 127, 255, cv2.THRESH_BINARY)
         top_outer_mask = np.array(binary_body_mask)
@@ -88,7 +88,7 @@ def synthesis(data, size, basic_info):
             if top and data[i]['name'] in ["blouse_front", "outwear_front", "dress_front", "tops_front"]:
                 top = False
                 mask_shape = data[i]['mask'].shape
-                y_offset, x_offset = data[i]['position']
+                y_offset, x_offset = data[i]['adaptive_position']
                 # 初始化叠加区域的起始和结束位置
                 all_y_start, all_y_end, mask_y_start, mask_y_end = positioning(all_mask_shape=all_mask_shape[0], mask_shape=mask_shape[0], offset=y_offset)
                 all_x_start, all_x_end, mask_x_start, mask_x_end = positioning(all_mask_shape=all_mask_shape[1], mask_shape=mask_shape[1], offset=x_offset)
@@ -100,7 +100,7 @@ def synthesis(data, size, basic_info):
             elif bottom and data[i]['name'] in ["trousers_front", "skirt_front", "bottoms_front", "dress_front"]:
                 bottom = False
                 mask_shape = data[i]['mask'].shape
-                y_offset, x_offset = data[i]['position']
+                y_offset, x_offset = data[i]['adaptive_position']
                 # 初始化叠加区域的起始和结束位置
                 all_y_start, all_y_end, mask_y_start, mask_y_end = positioning(all_mask_shape=all_mask_shape[0], mask_shape=mask_shape[0], offset=y_offset)
                 all_x_start, all_x_end, mask_x_start, mask_x_end = positioning(all_mask_shape=all_mask_shape[1], mask_shape=mask_shape[1], offset=x_offset)
@@ -118,13 +118,13 @@ def synthesis(data, size, basic_info):
             if layer['image'] is not None:
                 if layer['name'] != "body":
                     test_image = Image.new('RGBA', size, (0, 0, 0, 0))
-                    test_image.paste(layer['image'], (layer['position'][1], layer['position'][0]), layer['image'])
+                    test_image.paste(layer['image'], (layer['adaptive_position'][1], layer['adaptive_position'][0]), layer['image'])
                     mask_data = np.where(all_mask > 0, 255, 0).astype(np.uint8)
                     mask_alpha = Image.fromarray(mask_data)
                     cropped_image = Image.composite(test_image, Image.new("RGBA", test_image.size, (255, 255, 255, 0)), mask_alpha)
                     base_image.paste(test_image, (0, 0), cropped_image)  # test_image 已经按照坐标贴到最大宽值的图片上 坐着这里坐标为00
                 else:
-                    base_image.paste(layer['image'], (layer['position'][1], layer['position'][0]), layer['image'])
+                    base_image.paste(layer['image'], (layer['adaptive_position'][1], layer['adaptive_position'][0]), layer['image'])
 
         result_image = base_image