diff --git a/智数员工/zhaopin_zaoshu.py b/智数员工/zhaopin_zaoshu.py
new file mode 100644
index 0000000..0422cd4
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+++ b/智数员工/zhaopin_zaoshu.py
@@ -0,0 +1,205 @@
+import json
+import random
+import asyncio
+from typing import List, Dict, Any, Tuple
+from concurrent.futures import ThreadPoolExecutor
+import pandas as pd
+from openpyxl import Workbook
+import requests
+import logging
+logging.basicConfig(level=logging.INFO)
+# 配置参数
+class Config:
+    OUTPUT_FILE = "recruitment_data.xlsx"
+    FIXED_QUESTIONS = [
+        "上个月面试了多少人",
+        "本周安排了几个面试",
+        "招聘进度如何",
+        "有多少候选人进入二面",
+        "销售岗位的招聘情况",
+        "技术岗位的简历筛选数量",
+        "最近一周的offer发放数量",
+        "哪个部门的招聘完成率最高",
+        "招聘成本是否超出预算",
+        "候选人平均面试周期是多长"
+    ]
+    LOCATIONS = ["北京", "上海", "广州", "深圳", "杭州", "", "成都"]
+    INTENTS = ["招聘数据", "招聘进度", "其他", "成本分析", "效率统计"]
+    COMMISSIONER_TYPES = ["yxz", "hrbp", "recruiter", "manager"]
+    USER_NAMES = ["张招聘", "李HR", "王人事", "赵经理", "刘专员"]
+
+
+
+
+async def chat(input_content):
+        response = requests.post(
+            api_url = "http://100.105.1.227:8000/v1/chat/completions",
+            headers = {
+        "Content-Type": "application/json",
+        "Authorization": "7c3eafb5-2d6e-100d-ab0f-7b2c1cdafb3c"
+    },
+            json={
+                "model": "Qwen3-72B",
+                "stream": False,
+                "temperature": 0.6,
+                "TopP": 0.95,
+                "TopK": 20,
+                "MinP": 0,
+                "messages": [{"role": "user", "content": input_content}]
+            },
+            timeout=180
+        )
+        
+        if response.status_code == 200:
+            try:
+                result = response.json()["choices"][0]["message"]["content"]         
+            except Exception as e:
+                logging.error(f"Error processing API response: {e}")
+        else:
+            logging.error(f"API request failed with status code: {response.status_code}")
+        await asyncio.sleep(0.1)
+        return result
+
+
+
+# 模拟模型生成多样化问题
+async def generate_diverse_questions() -> List[str]:
+    # 这里应该是实际调用模型生成多样化问题的代码
+    # 模拟生成几个变体问题
+
+    input_content = """你是一个资深HR分析师。请生成一个招聘数据分析的查询请求，要求：
+- 聚焦在以下至少一个方面：面试、offer、入职、渠道效果、成本、周期时间
+- 包含具体的时间范围（如最近一周/上月/本季度）
+- 可选项包含部门/岗位/地域等维度
+- 直接返回问题，不要任何解释
+
+例如：
+对比北京和上海地区过去两个月销售岗位的offer接受率"""
+    gen_question = chat(input_content)
+    await asyncio.sleep(0.1)
+      
+    return gen_question
+
+
+
+# 生成招聘相关的输入数据
+async def generate_input_data(use_fixed: bool = True) -> Dict[str, Any]:
+    if random.random() > 0.3:
+        base_question = random.choice(Config.FIXED_QUESTIONS)
+    else:
+        
+        base_question = await generate_diverse_questions()
+
+    
+    return {
+        "messages": [{
+            "role": "user",
+            "content": base_question
+        }],
+        "location": random.choice(Config.LOCATIONS),
+        "uuid": str(random.randint(1e18, 1e19-1)),
+        "intent": random.choice(Config.INTENTS),
+        "loginUserName": random.choice(Config.USER_NAMES),
+        "loginUserId": "hr_" + str(random.randint(1000, 9999)),
+        "commissioner_type": random.choice(Config.COMMISSIONER_TYPES)
+    }
+
+# 处理单个请求
+async def process_request(input_data: Dict[str, Any]) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+    try:
+        input_content = f"""
+你是一个专业招聘数据分析助手。请按以下规则处理问题：
+1. 如果问题已包含明确且可清晰回答，直接返回原问题
+2. 如果问题模糊或不完整，按标准改写：
+   - 补充时间范围（最近/上月/本季度等）
+   - 明确量化指标（数量/比率/趋势等）
+   - 指定具体对象（岗位/部门/渠道等）
+3. 直接返回最终问题，不要任何解释
+
+待处理问题：{input_data}
+"""
+        user_content = input_data["messages"][0]["content"]
+        rewritten_question = await chat(input_content)
+        
+        output_data = {
+            "code": "0",
+            "message": "",
+            "result": rewritten_question
+        }
+        return input_data, output_data
+    except Exception as e:
+        output_data = {
+            "code": "1",
+            "message": str(e),
+            "result": ""
+        }
+        return input_data, output_data
+
+# 保存数据到Excel
+def save_to_excel(data: List[Dict[str, Any]], filename: str):
+    rows = []
+    for item in data:
+        input_data = item["input"]
+        output_data = item["output"]
+        
+        row = {
+            "输入问题": input_data["messages"][0]["content"],
+            "输出问题": output_data["result"],
+            "地点": input_data["location"],
+            "UUID": input_data["uuid"],
+            "意图": input_data["intent"],
+            "用户名": input_data["loginUserName"],
+            "用户ID": input_data["loginUserId"],
+            "专员类型": input_data["commissioner_type"],
+            "状态码": output_data["code"],
+            "消息": output_data["message"]
+        }
+        rows.append(row)
+    
+    df = pd.DataFrame(rows)
+    df.to_excel(filename, index=False, engine='openpyxl')
+    print(f"数据已保存到 {filename}")
+
+# 并发生成数据
+async def generate_data(num_samples: int) -> List[Dict[str, Any]]:
+    # 首先生成所有输入数据
+    input_tasks = [generate_input_data() for _ in range(num_samples)]
+    input_data_list = await asyncio.gather(*input_tasks)
+    
+    # 然后并发处理所有请求
+    process_tasks = [process_request(input_data) for input_data in input_data_list]
+    results = await asyncio.gather(*process_tasks)
+    
+    # 组合结果
+    output = []
+    for input_data, output_data in results:
+        output.append({
+            "input": input_data,
+            "output": output_data
+        })
+    
+    return output
+
+# 主函数
+async def main():
+    try:
+        
+        num_samples = 2000
+        print(f"开始生成 {num_samples} 条招聘数据...")
+        data_pairs = await generate_data(num_samples)
+        
+        save_to_excel(data_pairs, Config.OUTPUT_FILE)
+        
+        # 打印前3条样本
+        print("\n样本示例:")
+        for i, pair in enumerate(data_pairs[:3], 1):
+            print(f"样本 {i}:")
+            print("输入问题:", pair["input"]["messages"][0]["content"])
+            print("输出问题:", pair["output"]["result"])
+            print("-" * 50)
+            
+    except Exception as e:
+        print(f"发生错误: {e}")
+
+if __name__ == "__main__":
+    asyncio.run(main())
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