diff --git a/高考志愿查询.ipynb b/高考志愿查询.ipynb
index e45ca5a..436425f 100644
--- a/高考志愿查询.ipynb
+++ b/高考志愿查询.ipynb
@@ -18,10 +18,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "be2371be-89f5-4879-82a9-4a1d9e6376d3",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-07-03T13:57:31.935033Z",
+     "iopub.status.busy": "2023-07-03T13:57:31.934515Z",
+     "iopub.status.idle": "2023-07-03T13:57:31.994646Z",
+     "shell.execute_reply": "2023-07-03T13:57:31.993752Z",
+     "shell.execute_reply.started": "2023-07-03T13:57:31.934988Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 西北工业大学 计算机类（卓越班） 4046\n",
+      "2 厦门大学 计算机类 4369\n",
+      "3 北京交通大学 计算机类 4571\n",
+      "4 四川大学 计算机类 4583\n",
+      "5 西南交通大学 计算机类 4851\n",
+      "6 山东大学 计算机类（计算机与智能方向） 5486\n",
+      "7 西北工业大学 计算机类 5506\n",
+      "8 中南大学 计算机类（计算机信息类） 5632\n",
+      "9 湖南大学 计算机科学与技术（拔尖实验班） 5793\n",
+      "10 中国农业大学 计算机类 5808\n",
+      "11 大连理工大学 电子信息类（电信、自动化与计算机工科实验班） 6303\n",
+      "12 北京科技大学 计算机类 6789\n",
+      "13 山东大学 计算机类（软件数媒与大数据方向） 7425\n",
+      "14 重庆大学 计算机类（计算机与软件） 7581\n",
+      "15 南京航空航天大学 计算机科学与技术 7943\n",
+      "16 湖南大学 计算机科学与技术 8062\n",
+      "17 西安电子科技大学 计算机类 8528\n",
+      "18 湖南大学 计算机科学与技术（智能班） 8702\n",
+      "19 吉林大学 计算机类 8724\n",
+      "20 哈尔滨工业大学（威海） 计算机类 8895\n"
+     ]
+    }
+   ],
    "source": [
     "import pymongo\n",
     "import re\n",
@@ -30,9 +66,9 @@
     "mydb = myclient[\"gaokao\"]\n",
     "mycol = mydb[\"admission_2020\"]\n",
     "\n",
-    "m_zhuanye = '医学'\n",
-    "m_rank1 = 11000\n",
-    "m_rank2 = 15000\n",
+    "m_zhuanye = '计算机'\n",
+    "m_rank1 = 4000\n",
+    "m_rank2 = 9000\n",
     "\n",
     "myquery = {'spe_name':re.compile(m_zhuanye),'rank_min':{\"$gte\": m_rank1,\"$lte\": m_rank2}}\n",
     "i = 1\n",
@@ -51,20 +87,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "id": "07dbb641-d594-4c7c-92da-045a5370e6e9",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-28T02:27:08.902334Z",
+     "iopub.status.busy": "2023-06-28T02:27:08.901812Z",
+     "iopub.status.idle": "2023-06-28T02:27:08.944814Z",
+     "shell.execute_reply": "2023-06-28T02:27:08.943941Z",
+     "shell.execute_reply.started": "2023-06-28T02:27:08.902286Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 北京师范大学 历史学类 639\n",
+      "2 南开大学 历史学类 634\n",
+      "3 四川大学 历史学类 633\n",
+      "4 华东师范大学 历史学 632\n",
+      "5 中山大学 历史学(广州) 630\n",
+      "6 中国社会科学院大学 历史学 622\n",
+      "7 湖南大学 历史学 618\n",
+      "8 山东大学 历史学类 616\n",
+      "9 华中师范大学 历史学类 615\n",
+      "10 中央民族大学 历史学类 614\n",
+      "11 南京师范大学 历史学类 607\n"
+     ]
+    }
+   ],
    "source": [
     "import pymongo\n",
     "import re\n",
     "\n",
     "myclient = pymongo.MongoClient('mongodb://localhost:27017/')\n",
     "mydb = myclient[\"gaokao\"]\n",
-    "mycol = mydb[\"admission_2020\"]\n",
+    "mycol = mydb[\"admission_2022\"]\n",
     "\n",
-    "m_zhuanye = '医学'\n",
-    "m_fenshu = 600\n",
+    "m_zhuanye = '历史'\n",
+    "m_fenshu = 607\n",
     "\n",
     "myquery = {'spe_name':re.compile(m_zhuanye),'num_min':{\"$gte\": m_fenshu}}\n",
     "i = 1\n",
@@ -72,6 +135,14 @@
     "    print(i,x['col_name'],x['spe_name'],x['num_min'])\n",
     "    i += 1\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1778e718-d258-4a53-bb14-f82e09211fb7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/高考数据导入.ipynb b/高考数据导入.ipynb
index e81b3a9..a35db2a 100644
--- a/高考数据导入.ipynb
+++ b/高考数据导入.ipynb
@@ -1299,26 +1299,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2022-08-04T03:53:00.735786Z",
-     "iopub.status.busy": "2022-08-04T03:53:00.735257Z",
-     "iopub.status.idle": "2022-08-04T03:53:10.194351Z",
-     "shell.execute_reply": "2022-08-04T03:53:10.192776Z",
-     "shell.execute_reply.started": "2022-08-04T03:53:00.735737Z"
-    },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ok\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pymysql\n",
     "import pymongo\n",
@@ -1376,6 +1361,191 @@
     "print('ok')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 导入2023年录取数据"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 导入2023年一分一段表"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import openpyxl\n",
+    "import json\n",
+    "\n",
+    "\n",
+    "wb = openpyxl.load_workbook('./data/2023年一分一段表.xlsx')\n",
+    "sheet = wb.active\n",
+    "i = 1\n",
+    "dict1 = {}\n",
+    "for n in range(4,sheet.max_row):\n",
+    "        m_score = sheet.cell(n,1).value\n",
+    "        m_num = sheet.cell(n,2).value\n",
+    "        m_sum = sheet.cell(n,3).value\n",
+    "        m_max = m_sum - m_num + 1\n",
+    "        dict1[m_score] = {'num_person':m_num,'max_rank':m_max,'min_rank':m_sum}\n",
+    "filename = 'data/2023年一分一段表.json'\n",
+    "with open(filename,'w') as fl:\n",
+    "    json.dump(dict1,fl) \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-07-26T13:07:34.650896Z",
+     "iopub.status.busy": "2023-07-26T13:07:34.650369Z",
+     "iopub.status.idle": "2023-07-26T13:07:34.656873Z",
+     "shell.execute_reply": "2023-07-26T13:07:34.655204Z",
+     "shell.execute_reply.started": "2023-07-26T13:07:34.650847Z"
+    }
+   },
+   "source": [
+    "### 导出2023年录取数据并生成分数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import openpyxl\n",
+    "import json\n",
+    "\n",
+    "filename = 'data/2023年一分一段表.json'\n",
+    "with open(filename,'r') as fl:\n",
+    "    m_xx = json.load(fl)\n",
+    "\n",
+    "for k, v in m_xx.items():\n",
+    "    list1 = []\n",
+    "    list1 = (v['min_rank'],v['max_rank'])\n",
+    "    yfyd[int(k)] = list1\n",
+    "dict1 = {}\n",
+    "filename = 'data/山东省2023年普通类常规批第1次志愿投档情况表.xlsx'\n",
+    "wb = openpyxl.load_workbook(filename)\n",
+    "sheet = wb.active\n",
+    "data1 =list(sheet.values)\n",
+    "del data1[0:2]\n",
+    "m_num = 1\n",
+    "i = 1\n",
+    "for items in data1:\n",
+    "    m_rank = items[3]    \n",
+    "    list1 = []\n",
+    "    print(items[0])\n",
+    "    for k, v in yfyd.items():\n",
+    "        if m_rank >=v[1] and m_rank <=v[0]:\n",
+    "            m_num = int(k)\n",
+    "            break\n",
+    "    list1 = (items[0],items[1],items[2],items[3],m_num)\n",
+    "    dict1[i] = list1\n",
+    "    i += 1 \n",
+    "with open('data/2023.json','w') as fl2:\n",
+    "    json.dump(dict1,fl2) \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2023年专业录取分数及位次"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-07-26T14:40:10.969625Z",
+     "iopub.status.busy": "2023-07-26T14:40:10.969096Z",
+     "iopub.status.idle": "2023-07-26T14:40:29.882965Z",
+     "shell.execute_reply": "2023-07-26T14:40:29.881763Z",
+     "shell.execute_reply.started": "2023-07-26T14:40:10.969577Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ok\n",
+      "ok\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "import pymongo\n",
+    "import decimal\n",
+    "\n",
+    "myclient = pymongo.MongoClient('mongodb://localhost:27017/')\n",
+    "mydb = myclient[\"gaokao\"]\n",
+    "mycol1 = mydb[\"admission_ls\"]\n",
+    "mycol = mydb[\"admission_2023\"]\n",
+    "\n",
+    "m_col = {}\n",
+    "m_spe = {}\n",
+    "m_xx = {}\n",
+    "filename = 'data/2023.json'\n",
+    "with open(filename,'r') as fl:\n",
+    "    dict1 = json.load(fl)\n",
+    "\n",
+    "for k, v in dict1.items():\n",
+    "    m_xx = {}\n",
+    "    m_xx['col_code'] = v[1][:4]\n",
+    "    m_xx['col_name'] = v[1][4:]\n",
+    "    m_xx['spe_code'] = v[0][:2]\n",
+    "    m_xx['spe_name'] = v[0][2:]\n",
+    "    m_xx['plan'] = v[2]\n",
+    "    m_xx['num_min'] = v[4] \n",
+    "    m_xx['rank_min'] = v[3]\n",
+    "    m_xx['nian'] = '2023'\n",
+    "    mycol1.insert_one(m_xx)\n",
+    "    #print(m_xx)\n",
+    "print('ok')\n",
+    "mydoc = mycol1.find().sort(\"rank_min\")\n",
+    "i = 1 \n",
+    "for x in mydoc:\n",
+    "    m_xx = {}\n",
+    "    m_xx['pos'] = i\n",
+    "    m_xx['col_code'] = x['col_code']\n",
+    "    m_xx['col_name'] = x['col_name'] \n",
+    "    m_xx['spe_code'] = x['spe_code']\n",
+    "    m_xx['spe_name'] = x['spe_name']\n",
+    "    m_xx['plan'] = x['plan']\n",
+    "    m_xx['num_min'] = x['num_min']\n",
+    "    m_xx['rank_min'] = x['rank_min']\n",
+    "    m_xx['nian'] = '2023'\n",
+    "    mycol.insert_one(m_xx)\n",
+    "    i+=1\n",
+    "print('ok')    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {