From 4093ba4ad5fabe1c5781e4c39ee7f71dfeae3c1f Mon Sep 17 00:00:00 2001
From: HuangHai <10402852@qq.com>
Date: Sat, 8 Mar 2025 13:38:01 +0800
Subject: [PATCH] 'commit'

---
 AI/AiService/MarkdownToJsonConverter.py       |  22 ++-
 AI/AiService/Start.py                         |  27 ++--
 .../MarkdownToJsonConverter.cpython-310.pyc   | Bin 5331 -> 5303 bytes
 AI/AiService/getExcel.py                      |  65 ++++++++
 AI/AiService/部门项目总览.xlsx          | Bin 0 -> 12317 bytes
 AI/Text2Sql/Sql/CreateTable.sql               |   4 +-
 AI/Text2Sql/YunXiao_Deepseek.py               |  65 ++++++++
 AI/Text2Sql/YunXiao_Vanna.py                  | 139 ++++++++++++++++++
 .../java/Tools/Crawler/Util/BookLesson.java   |   3 -
 9 files changed, 303 insertions(+), 22 deletions(-)
 create mode 100644 AI/AiService/getExcel.py
 create mode 100644 AI/AiService/部门项目总览.xlsx
 create mode 100644 AI/Text2Sql/YunXiao_Deepseek.py
 create mode 100644 AI/Text2Sql/YunXiao_Vanna.py

diff --git a/AI/AiService/MarkdownToJsonConverter.py b/AI/AiService/MarkdownToJsonConverter.py
index 8cf28b36..94f14c22 100644
--- a/AI/AiService/MarkdownToJsonConverter.py
+++ b/AI/AiService/MarkdownToJsonConverter.py
@@ -7,8 +7,9 @@ from CommonUtil import *
 
 
 class MarkdownToJsonConverter:
-    def __init__(self, client):
+    def __init__(self, client, language):
         self.client = client
+        self.language = language
 
     def extract_level1_title(self, markdown_content):
         """
@@ -110,36 +111,41 @@ class MarkdownToJsonConverter:
 
         return result
 
-    def generate_descriptions_for_json_batch(self, json_data):
+    def generate_descriptions_for_json_batch(self, json_data,language):
         """
         批量生成描述语句，并替换 JSON 中的 text 属性
         """
         for item in json_data:
             if "data" in item and "title" in item["data"]:
                 title = item["data"]["title"]
-                description = self.generate_description(title)  # 同步调用
+                description = self.generate_description(title,language)  # 同步调用
                 item["data"]["text"] = description
 
             if "data" in item and "items" in item["data"]:
                 for sub_item in item["data"]["items"]:
                     if "title" in sub_item:
                         title = sub_item["title"]
-                        description = self.generate_description(title)  # 同步调用
+                        description = self.generate_description(title,language)  # 同步调用
                         sub_item["text"] = description
             yield json.dumps(item, ensure_ascii=False)
             time.sleep(0.5)  # 控制逐行输出的速度
 
-    def generate_description(self, title):
+    def generate_description(self, title,language):
         """
         调用 AI 接口，生成描述语句（限制在 20 个字以内）
         """
+        prompt=''
         try:
+            if language=='cn':
+                prompt='请使用中文回答。'
+            elif language=='en':
+                prompt='Please answer in English.'
             response = client.chat.completions.create(  # 同步调用
                 model=MODEL_NAME,
                 messages=[
                     {"role": "system", "content": "你是一个专业的助手，能够根据上下文生成简洁的描述信息。"},
                     {"role": "user",
-                     "content": f"请为以下标题生成一句话的描述信息，描述信息应简洁明了，且与标题内容相关，不要使用与标题内容相同的语句，不要包含任何序号（如 1.、2. 等）或 Markdown 语法（如 #、- 等），且描述长度不超过 20 个字：\n- {title}"}
+                     "content": f"{prompt}请为以下标题生成一句话的描述信息，描述信息应简洁明了，且与标题内容相关，不要使用与标题内容相同的语句，不要包含任何序号（如 1.、2. 等）或 Markdown 语法（如 #、- 等），且描述长度不超过 20 个字：\n- {title}"}
                 ],
                 max_tokens=20
             )
@@ -156,7 +162,7 @@ class MarkdownToJsonConverter:
             print(f"调用 AI 生成描述信息时出错：{e}")
             return title
 
-    def convert_markdown_to_json(self, markdown_content):
+    def convert_markdown_to_json(self, markdown_content,language):
         """
         将 Markdown 内容转换为 JSON 格式
         """
@@ -179,5 +185,5 @@ class MarkdownToJsonConverter:
             listAll.append(item)
 
         # 生成描述
-        for item in self.generate_descriptions_for_json_batch(listAll):  # 使用普通 for 循环
+        for item in self.generate_descriptions_for_json_batch(listAll,language):  # 使用普通 for 循环
             yield item
diff --git a/AI/AiService/Start.py b/AI/AiService/Start.py
index 67f64a6c..b0b783bf 100644
--- a/AI/AiService/Start.py
+++ b/AI/AiService/Start.py
@@ -20,11 +20,11 @@ client = OpenAI(
 )
 
 
-def convertMarkdownToJson(markdown_content):
+def convertMarkdownToJson(markdown_content: str, language: str):
     # 创建转换器实例
-    converter = MarkdownToJsonConverter(client)
+    converter = MarkdownToJsonConverter(client, language)
     # 转换 Markdown 为 JSON
-    for item in converter.convert_markdown_to_json(markdown_content):  # 使用普通 for 循环
+    for item in converter.convert_markdown_to_json(markdown_content,language):  # 使用普通 for 循环
         yield f"{item}\n"  # 每行数据后添加换行符
     # 添加结束标记
     yield '{"type": "end" }\n'  # 结束标记后也添加换行符
@@ -47,17 +47,22 @@ def get_local_ips():
 
 
 # 流式生成数据的函数
-async def generate_stream_markdown(course_name: str):
+async def generate_stream_markdown(course_name: str, language: str):
     """
     流式生成 Markdown 数据，并在控制台输出完整的 Markdown 内容
     """
+    prompt = ''
+    if language == 'cn':
+        prompt = '请使用中文回答'
+    elif language == 'en':
+        prompt = 'Please answer in English。'
     # 调用阿里云 API，启用流式响应
     stream = client.chat.completions.create(
         model=MODEL_NAME,
         messages=[
             {'role': 'system', 'content': '你是一个教学经验丰富的基础教育教师'},
             {'role': 'user',
-             'content': '帮我设计一下' + course_name + '的课件提纲，用markdown格式返回。强调1、标签只能返回 #,##,###,-,其它标签一率不可以返回，这个非常重要！2、不要返回 ```markdown 或者 ```  这样的内容！ 3、每部分都有生成完整的一、二、三级内容，不能省略。'}
+             'content': prompt + ',帮我设计一下' + course_name + '的课件提纲，用markdown格式返回。强调1、标签只能返回 #,##,###,-,其它标签一率不可以返回，这个非常重要！2、不要返回 ```markdown 或者 ```  这样的内容！ 3、每部分都有生成完整的一、二、三级内容，不能省略。'}
         ],
         stream=True,  # 启用流式响应
         timeout=6000,
@@ -100,11 +105,12 @@ def root():
 
 @app.post("/api/tools/aippt_outline")  # 仅支持 POST 方法
 async def aippt_outline(
-        course_name: str = Body(..., embed=True, description="课程名称")  # 从请求体中获取 course_name
+        course_name: str = Body(..., embed=True, description="课程名称"),  # 从请求体中获取 course_name
+        language: str = Body(..., embed=True, description="语言"),
 ):
     # 返回流式响应
     return StreamingResponse(
-        generate_stream_markdown(course_name),
+        generate_stream_markdown(course_name, language),
         media_type="text/event-stream",
         headers={
             "Cache-Control": "no-cache",
@@ -116,9 +122,10 @@ async def aippt_outline(
 
 
 @app.post("/api/tools/aippt")  # 修改为 POST 方法
-def aippt(content: str = Body(..., embed=True, description="Markdown 内容")):  # 使用 Body 接收请求体参数
+def aippt(content: str = Body(..., embed=True, description="Markdown 内容"),
+          language: str = Body(..., embed=True, description="语言")):  # 使用 Body 接收请求体参数
     return StreamingResponse(
-        convertMarkdownToJson(content),  # 传入 content
+        convertMarkdownToJson(content, language),  # 传入 content
         media_type="text/plain",  # 使用 text/plain 格式
         headers={
             "Cache-Control": "no-cache",
@@ -144,4 +151,4 @@ if __name__ == "__main__":
         print(f"http://{ip}:5173")
 
     # 启动 FastAPI 应用，绑定到所有 IP 地址
-    uvicorn.run(app, host="0.0.0.0", port=5173)
\ No newline at end of file
+    uvicorn.run(app, host="0.0.0.0", port=5173)
diff --git a/AI/AiService/__pycache__/MarkdownToJsonConverter.cpython-310.pyc b/AI/AiService/__pycache__/MarkdownToJsonConverter.cpython-310.pyc
index 744585530d957c9561ce12a0a349c9642b54b2f7..5fae9c805620cb94983f042c3a4738058fa78797 100644
GIT binary patch
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diff --git a/AI/AiService/getExcel.py b/AI/AiService/getExcel.py
new file mode 100644
index 00000000..d43494a2
--- /dev/null
+++ b/AI/AiService/getExcel.py
@@ -0,0 +1,65 @@
+import pandas as pd
+from openpyxl import load_workbook
+from openpyxl.styles import PatternFill
+from openpyxl.formatting.rule import ColorScaleRule
+
+# ================= 创建测试数据 =================
+project_data = {
+    "项目编号": ["P001", "P002"],
+    "项目名称": ["CRM系统升级", "电商平台开发"],
+    "项目类型": ["升级", "新开发"],
+    "起止时间": ["2025-03-01 ~ 2025-06-30", "2025-04-01 ~ 2025-12-31"],
+    "当前阶段": ["开发中", "需求确认"],
+    "交付状态": ["未交付", "部分交付"]
+}
+
+progress_data = {
+    "项目编号": ["P001", "P002"],
+    "整体进度(%)": [45, 20],
+    "剩余人天": [120, 300],
+    "当前人数": [8, 5],
+    "资源缺口": [2, 3]
+}
+
+maintenance_data = {
+    "产品名称": ["CRM系统", "订单管理模块"],
+    "维护类型": ["付费维护", "质保期维护"],
+    "工单量": [15, 8],
+    "平均耗时(h)": [3.5, 2.0]
+}
+
+product_data = {
+    "产品名称": ["CRM标准版", "ERP基础版"],
+    "定价(万元)": [12.8, 28.5],
+    "近半年销量": [35, 18]
+}
+
+# ================= 生成Excel文件 =================
+with pd.ExcelWriter("部门项目总览.xlsx", engine="openpyxl") as writer:
+    # 生成四个工作表
+    pd.DataFrame(project_data).to_excel(writer, sheet_name="项目概况", index=False)
+    pd.DataFrame(progress_data).to_excel(writer, sheet_name="进度管理", index=False)
+    pd.DataFrame(maintenance_data).to_excel(writer, sheet_name="维护工作", index=False)
+    pd.DataFrame(product_data).to_excel(writer, sheet_name="产品信息", index=False)
+
+    # 获取工作簿对象进行格式设置
+    workbook = writer.book
+
+    # 设置公共格式
+    for sheetname in writer.sheets:
+        sheet = writer.sheets[sheetname]
+        # 冻结首行
+        sheet.freeze_panes = "A2"
+        # 设置列宽
+        sheet.column_dimensions['A'].width = 15
+        sheet.column_dimensions['B'].width = 25
+
+    # 为进度表添加条件格式
+    progress_sheet = writer.sheets["进度管理"]
+    progress_rule = ColorScaleRule(start_type='num', start_value=0, start_color='FF0000',
+                                   mid_type='num', mid_value=50, mid_color='FFFF00',
+                                   end_type='num', end_value=100, end_color='00FF00')
+    progress_sheet.conditional_formatting.add("B2:B100", progress_rule)
+
+# ================= 最终文件生成 =================
+print("文件已生成：部门项目总览.xlsx")
diff --git a/AI/AiService/部门项目总览.xlsx b/AI/AiService/部门项目总览.xlsx
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diff --git a/AI/Text2Sql/Sql/CreateTable.sql b/AI/Text2Sql/Sql/CreateTable.sql
index 80308afe..cfe0c90f 100644
--- a/AI/Text2Sql/Sql/CreateTable.sql
+++ b/AI/Text2Sql/Sql/CreateTable.sql
@@ -168,4 +168,6 @@ COMMENT ON COLUMN "public"."t_crawler_structure_knowledge"."subject_name" IS '
  用得到stage_id再与其它表进行关联查询，不要直接使用学段名称进行查询。
 2、如果用户需要检索科目，比如语文，数学，需要先到
  select subject_id from t_crawler_subject where subject_name='语文'
- 用得到subject_id再与其它表进行关联查询，不要直接使用科目名称进行查询。
\ No newline at end of file
+ 用得到subject_id再与其它表进行关联查询，不要直接使用科目名称进行查询。
+3、凡是涉及到行政区划,也就是gather_regionc字段的，不能直接从t_crawler_lesson表中读取，它没有这个列，
+需要通过lesson_id 关联到t_crawler_lesson_school表中，再从t_crawler_lesson_school表中读取。
\ No newline at end of file
diff --git a/AI/Text2Sql/YunXiao_Deepseek.py b/AI/Text2Sql/YunXiao_Deepseek.py
new file mode 100644
index 00000000..57225e89
--- /dev/null
+++ b/AI/Text2Sql/YunXiao_Deepseek.py
@@ -0,0 +1,65 @@
+from openai import OpenAI
+
+# 阿里云中用来调用 deepseek v3 的密钥
+MODEL_API_KEY = "sk-01d13a39e09844038322108ecdbd1bbc"
+#MODEL_NAME = "qwen-plus"
+MODEL_NAME="deepseek-v3"
+
+# 初始化 OpenAI 客户端
+client = OpenAI(
+    api_key=MODEL_API_KEY,
+    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+)
+
+def generate_sql_from_prompt(ddl: str, prompt: str) -> str:
+    """
+    根据 DDL 和自然语言描述生成 SQL 查询
+    :param ddl: 数据库表结构的 DDL
+    :param prompt: 自然语言描述
+    :return: 生成的 SQL 查询
+    """
+    # 构建完整的提示词
+    full_prompt = (
+        f"以下是数据库表结构的 DDL：\n\n{ddl}\n\n"
+        f"请根据以下描述生成 SQL 查询：\n\n{prompt}\n\n"
+        "生成的 SQL 查询："
+    )
+
+    # 调用大模型
+    response = client.chat.completions.create(
+        model=MODEL_NAME,
+        messages=[
+            {"role": "system", "content": "你是一个专业的 SQL 生成助手，能够根据数据库表结构和自然语言描述生成正确的 SQL 查询。"},
+            {"role": "user", "content": full_prompt}
+        ],
+        max_tokens=500
+    )
+
+    # 提取生成的 SQL
+    if response.choices and response.choices[0].message.content:
+        return response.choices[0].message.content.strip()
+    else:
+        raise ValueError("未能生成 SQL 查询")
+
+if __name__ == '__main__':
+    # 读取 Sql/CreateTable.sql 文件
+    with open("Sql/CreateTable.sql", "r", encoding="utf-8") as file:
+        ddl = file.read()
+
+    # 自然语言描述
+    prompt = "查询 2024 年每个学段下，上传课程数量排名前 10 的学校，并按行政区名称和上传课程数量排序。"
+
+    common_prompt='''
+    要求：
+    1、对于学校名称和行政区划名称为空的不要进行统计。
+    2、有行政区划列返回时，先按行政区划排序
+    3、有课程数量时，再按课程数量由高到低排序
+    '''
+    prompt = prompt + common_prompt
+    # 生成 SQL
+    try:
+        sql = generate_sql_from_prompt(ddl, prompt)
+        print("生成的 SQL 查询：")
+        print(sql)
+    except Exception as e:
+        print(f"生成 SQL 时出错：{e}")
\ No newline at end of file
diff --git a/AI/Text2Sql/YunXiao_Vanna.py b/AI/Text2Sql/YunXiao_Vanna.py
new file mode 100644
index 00000000..ba7b81a4
--- /dev/null
+++ b/AI/Text2Sql/YunXiao_Vanna.py
@@ -0,0 +1,139 @@
+import os
+import platform
+from Text2Sql.Util.Text2SqlUtil import *
+from Text2Sql.Util.PostgreSQLUtil import PostgreSQLUtil
+from Text2Sql.Util.SaveToExcel import save_to_excel
+from pyecharts.charts import Bar
+from pyecharts import options as opts
+from pyecharts.commons.utils import JsCode
+import pandas as pd
+import webbrowser
+
+
+def process_data(raw_data):
+    """
+    处理原始数据：
+    1. 过滤无效行政区
+    2. 按上传数量降序排序
+    3. 取前10名
+    """
+    df = pd.DataFrame(raw_data)
+    df = df[df['行政区划名称'].notna() & (df['行政区划名称'] != '')]
+    df = df.sort_values('上传课程数量', ascending=False).head(10)
+    return df
+
+
+def create_top10_chart(data):
+    # 准备数据
+    schools = data['学校名称'].tolist()
+    counts = data['上传课程数量'].tolist()
+    districts = data['行政区划名称'].tolist()
+
+    # 生成颜色渐变（从深到浅）
+    colors = [f"rgb({75 + i * 15}, {115 - i * 10}, {220 - i * 20})" for i in range(10)]
+
+    # 创建图表
+    bar = Bar(init_opts=opts.InitOpts(width='1200px', height='600px'))
+    bar.add_xaxis(schools)
+    bar.add_yaxis(
+        series_name="上传数量",
+        y_axis=counts,
+        itemstyle_opts=opts.ItemStyleOpts(color=JsCode(
+            "function(params){"
+            f"  return {colors}[params.dataIndex];"
+            "}"
+        )),
+        label_opts=opts.LabelOpts(
+            position="right",
+            formatter=JsCode(
+                "function(params){"
+                "  return params.value + ' (' + params.name.split('').join('\\n') + ')';"
+                "}"
+            )
+        )
+    )
+
+    # 全局配置
+    bar.set_global_opts(
+        title_opts=opts.TitleOpts(
+            title="上传资源数量TOP10学校排名",
+            subtitle="数据来源：课程资源管理系统"
+        ),
+        tooltip_opts=opts.TooltipOpts(
+            formatter=JsCode(
+                """function(params){
+                    return params.name + '<br/>'
+                        + '所属行政区：' + %s[params.dataIndex] + '<br/>'
+                        + '上传数量：<b style="color:#5470C6">' + params.value + '</b>'
+                }""" % districts
+            )
+        ),
+        xaxis_opts=opts.AxisOpts(
+            axislabel_opts=opts.LabelOpts(
+                rotate=30,
+                formatter=JsCode(
+                    "function(value){"
+                    "  return value.length > 6 ? value.substring(0,6)+'...' : value;"
+                    "}"
+                )
+            )
+        ),
+        yaxis_opts=opts.AxisOpts(name="上传数量（件）"),
+        datazoom_opts=[opts.DataZoomOpts(type_="inside")],
+        visualmap_opts=opts.VisualMapOpts(
+            min_=min(counts),
+            max_=max(counts),
+            orient="horizontal",
+            pos_left="center",
+            range_color=["#91CC75", "#5470C6"]
+        )
+    )
+
+    # 反转Y轴使降序排列
+    bar.reversal_axis()
+
+    return bar
+
+
+if __name__ == "__main__":
+    vn = DeepSeekVanna()
+
+    # 开始训练
+    print("开始训练...")
+    # 打开CreateTable.sql文件内容
+    with open("Sql/CreateTable.sql", "r", encoding="utf-8") as file:
+        ddl = file.read()
+    # 训练数据
+    vn.train(
+        ddl=ddl
+    )
+
+    # 自然语言提问
+    # '''
+    question1 = '''
+        查询发布时间是2024年度，每个行政区划每个学校都上传了多少课程数量，
+        返回:行政区名称，学校名称，上传课程数量等属性.
+        '''
+    question = '''
+        查询发布时间是2024年度，按学段分组，比如小学、初中、高中，每个学段中上传课程数量前10名的都是些学校,
+        注意：排名是指分组内部排名，不是整体排名
+        返回: 学段，排名,行政区名称，学校名称，上传课程数量等属性.
+        '''
+    common_prompt = '''
+    要求：
+    1、行政区划为NULL 或者是空字符的不参加统计工作，
+    2、有行政区划列返回时，先按行政区划排序
+    3、有课程数量时，再按课程数量由高到低排序'''
+    question = question + common_prompt
+    # 开始查询
+    print("开始查询...")
+    # 获取完整 SQL
+    sql = vn.generate_sql(question)
+    print("生成的查询 SQL:\n", sql)
+
+    # 执行SQL查询
+    with PostgreSQLUtil() as db:
+        sample_data = db.execute_query(sql)
+        filename = "d:/导出信息.xlsx"
+        save_to_excel(sample_data, filename)
+        os.startfile(filename)
diff --git a/src/main/java/Tools/Crawler/Util/BookLesson.java b/src/main/java/Tools/Crawler/Util/BookLesson.java
index d78899e5..8e22e035 100644
--- a/src/main/java/Tools/Crawler/Util/BookLesson.java
+++ b/src/main/java/Tools/Crawler/Util/BookLesson.java
@@ -482,9 +482,6 @@ public class BookLesson {
         for (Record record : lessonList) {
             String original_school_name = record.getStr("teacher_school_name");//原始学校名称
 
-            if (original_school_name.equals("东北师范大学东安实验学校")) {
-                System.out.println("Here!");
-            }
             String teacher_name = record.getStr("teacher_name");
             String organization_name = "";
             String organization_no = "";