371 lines
14 KiB
Python
371 lines
14 KiB
Python
import requests
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import json
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import time
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import os
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from lxml import html
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import re
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import urllib.parse
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import pandas as pd # ★ 引入pandas用于处理多Sheet Excel
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# ================= 1. 配置区域 =================
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base_url = "http://111.198.24.44:88/index.php"
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# 登录参数
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login_payload = {
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"module": "Users",
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"action": "Authenticate",
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"return_module": "Users",
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"return_action": "Login",
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"user_name": "TEST", # 请填入真实用户名
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"user_password": "****", # 请填入真实密码
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"login_theme": "newskin"
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}
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# 全局 HTTP 请求头
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http_headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
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"Referer": "http://111.198.24.44:88/index.php?module=SalesOrder&action=index"
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}
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# ================= 2. 核心辅助函数 =================
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def get_current_action_id():
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"""生成当前时间的13位时间戳"""
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return int(time.time() * 1000)
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def clean_text_structure(element):
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"""深度清洗函数"""
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if element is None:
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return ""
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import copy
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el = copy.deepcopy(element)
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for bad_tag in el.xpath('.//script | .//style | .//noscript'):
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bad_tag.drop_tree()
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for br in el.xpath('.//br'):
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br.tail = "\n" + (br.tail if br.tail else "")
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text_content = el.text_content()
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lines = []
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for line in text_content.splitlines():
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clean_line = line.replace('\xa0', ' ').strip()
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if clean_line:
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lines.append(clean_line)
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return "\n".join(lines)
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def extract_html_content(html_content, xpath):
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try:
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tree = html.fromstring(html_content)
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elements = tree.xpath(xpath)
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if elements:
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target_element = elements[0]
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raw_html = html.tostring(target_element, encoding='unicode', pretty_print=True)
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cleaned_text = clean_text_structure(target_element)
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return {"raw_html": raw_html, "cleaned_text": cleaned_text}
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else:
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return {"raw_html": "", "cleaned_text": ""}
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except Exception as e:
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print(f" ❌ HTML解析错误: {e}")
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return {"raw_html": "", "cleaned_text": ""}
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def fetch_html_detail(session, record_id, xpath):
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try:
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url = f"http://111.198.24.44:88/index.php?module=SalesOrder&action=DetailView&record={record_id}"
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resp = session.get(url, headers=http_headers)
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if resp.status_code == 200:
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return extract_html_content(resp.content, xpath)
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return {"raw_html": "", "cleaned_text": ""}
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except Exception:
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return {"raw_html": "", "cleaned_text": ""}
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def extract_crmid_from_search_result(html_content):
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crmids = []
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try:
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tree = html.fromstring(html_content)
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links = tree.xpath('//div[@id="collapse-SalesOrder"]//a[contains(@onclick, "record=")]')
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if not links:
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links = tree.xpath('//a[contains(@onclick, "module=SalesOrder") and contains(@onclick, "record=")]')
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for link in links:
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onclick = link.get('onclick', '')
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match = re.search(r"record=(\d+)", onclick)
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if match:
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crmid = match.group(1)
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if crmid not in crmids:
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crmids.append(crmid)
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return crmids
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except Exception:
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return []
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def perform_search(session, query_string):
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try:
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search_url = f"http://111.198.24.44:88/index.php?module=Home&action=UnifiedSearch&selectedmodule=undefined&query_string={query_string}"
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resp = session.get(search_url, headers=http_headers)
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if resp.status_code == 200:
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return extract_crmid_from_search_result(resp.content)
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return []
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except Exception:
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return []
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# ================= 3. 核心解析逻辑 =================
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def parse_order_text(text):
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"""
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解析文本，返回通用字典
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"""
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if not text:
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return {}
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# 初始化通用字段池 (包含内贸和外贸所有可能用到的字段)
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data = {
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"合同编号": "", "内贸合同号": "", "外贸合同号": "",
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"签署公司": "", "收款情况": "", "签订日期": "", "销售员": "",
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"最终用户单位": "", "最终用户信息联系人": "", "最终用户信息电话": "", "最终用户信息邮箱": "",
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"最终用户所在地": "",
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"买方单位": "", "买方信息联系人": "", "买方信息电话": "", "买方信息邮箱": "",
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"厂家型号": "", "合同标的": "", "数量": "", "单位": "台/套",
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"折扣率（％）": "", "合同额": "", "合同总额": "",
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"外购付款方式": "", "最晚发货期": "",
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"已收款": "", "未收款": "", "收款日期": ""
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}
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lines = [line.strip() for line in text.split('\n') if line.strip()]
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# 映射表：文本中的Key -> 数据字典中的Key
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key_map = {
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"收款账户": "签署公司",
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"收款状态": "收款情况",
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"签约日期": "签订日期",
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"负责人": "销售员",
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"客户名称": "最终用户单位",
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"联系人姓名": "最终用户信息联系人",
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"合同总额": "合同总额",
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"最新收款日期": "收款日期",
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"最晚发货期": "最晚发货期",
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"付款比例及期限": "外购付款方式", # 这里对应您的要求
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"地址": "最终用户所在地"
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}
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for i, line in enumerate(lines):
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# 1.0 合同订单编号处理
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if line == "合同订单编号":
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if i + 1 < len(lines):
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full_val = lines[i + 1].strip()
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parts = full_val.split()
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if len(parts) >= 1:
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data["合同编号"] = parts[0]
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# 判断第二部分是内贸号还是外贸号，暂时先都存起来，在外面根据W/N区分
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if len(parts) >= 2:
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# 临时存储，稍后在 main 函数里根据 W/N 决定赋给谁
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data["_temp_second_code"] = parts[1]
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# 1.1 常规映射
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elif line in key_map:
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if i + 1 < len(lines):
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target_key = key_map[line]
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if not data[target_key]:
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data[target_key] = lines[i + 1]
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# 1.2 产品行解析
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elif "合同标的" in line and "品名/型号" in line:
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if i + 1 < len(lines):
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parts = lines[i + 1].split('/')
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# 格式假设: 标的/型号/数量/单价/总价
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if len(parts) >= 1: data["合同标的"] = parts[0]
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if len(parts) >= 2: data["厂家型号"] = parts[1]
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if len(parts) >= 3: data["数量"] = parts[2]
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if len(parts) >= 5: data["合同额"] = parts[4]
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# 1.3 折扣率 (如果有这个字段的话，通常在产品附近)
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# 这里假设如果没有明确字段，暂留空，或者您有特定的关键词提取逻辑
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# 2. 正则提取买方信息
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buyer_match = re.search(r"(?:买方|The Buyer)[：:]\s*(.*?)(?:\n|$)", text)
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if buyer_match and len(buyer_match.group(1)) > 1:
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data["买方单位"] = buyer_match.group(1).strip()
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buyer_contact = re.search(r"联系人（Contact person）[：:]\s*(.*?)(?:\n|$)", text)
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if buyer_contact:
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data["买方信息联系人"] = buyer_contact.group(1).strip()
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buyer_tel = re.search(r"电话\(Tel\)[：:]\s*(.*?)(?:\s+|$|传真)", text)
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if buyer_tel:
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data["买方信息电话"] = buyer_tel.group(1).strip()
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# 3. 计算已收/未收
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try:
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total = float(data["合同总额"]) if data["合同总额"] else 0
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status = data["收款情况"]
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if "已收" in status:
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data["已收款"] = str(total)
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data["未收款"] = "0"
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elif "未" in status:
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data["已收款"] = "0"
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data["未收款"] = str(total)
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except:
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pass
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return data
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# ================= 4. 主程序逻辑 =================
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def main():
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session = requests.Session()
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target_xpath = "/html/body/div[1]/div/div[2]/div[2]/form/div[1]/div[1]/div[2]"
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try:
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# --- 1. 登录 ---
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print("1. 正在登录...")
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session.post(base_url, data=login_payload, headers=http_headers)
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if 'PHPSESSID' in session.cookies:
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print(" ✅ 登录成功")
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else:
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print(" ⚠️ 警告: 未检测到Cookie，可能登录失败")
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# --- 2. 搜索 ---
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print("\n2. 请输入搜索内容:")
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query_input = input(" 搜索关键词: ").strip()
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if not query_input: return
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encoded_query = urllib.parse.quote(query_input)
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print(f"\n3. 执行搜索...")
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crmids = perform_search(session, encoded_query)
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if not crmids:
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print(" ❌ 未找到相关订单。")
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return
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print(f" ✅ 找到 {len(crmids)} 个订单 ID: {crmids}")
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# --- 3. 抓取与分类 ---
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print(f"\n4. 开始获取详情并分类处理...")
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# 定义三个列表用于存储不同类型的数据
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list_domestic = [] # 内贸 (N开头)
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list_foreign = [] # 外贸 (W开头)
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list_other = [] # 其他
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valid_count = 0
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for i, crmid in enumerate(crmids):
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print(f" [{i + 1}/{len(crmids)}] 处理 ID: {crmid}")
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html_data = fetch_html_detail(session, crmid, target_xpath)
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clean_text = html_data['cleaned_text']
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# 解析
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data = parse_order_text(clean_text)
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contract_no = data.get("合同编号", "").strip().upper() # 转大写处理
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# ★ 过滤空数据
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if not contract_no:
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print(f" ⚠️ 跳过: 未找到合同编号")
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continue
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# ★ 核心分类逻辑
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second_code = data.pop("_temp_second_code", "") # 取出临时存的第二段编号
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if contract_no.startswith('W'):
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# 外贸
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data['外贸合同号'] = second_code
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list_foreign.append(data)
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print(f" 🌍 归类: [外贸] {contract_no}")
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elif contract_no.startswith('N'):
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# 内贸
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data['内贸合同号'] = second_code
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list_domestic.append(data)
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print(f" 🏠 归类: [内贸] {contract_no}")
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else:
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# 其他
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data['内贸合同号'] = second_code # 默认存这里
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list_other.append(data)
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print(f" ❓ 归类: [其他] {contract_no}")
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valid_count += 1
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time.sleep(0.5)
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# --- 4. 导出 Excel (多Sheet) ---
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print(f"\n5. 正在导出 Excel 文件...")
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if valid_count == 0:
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print(" ❌ 无有效数据导出")
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return
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timestamp = time.strftime("%Y%m%d_%H%M%S")
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output_dir = f"Result_{timestamp}"
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os.makedirs(output_dir, exist_ok=True)
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xlsx_filename = os.path.join(output_dir, f"Export_{query_input}_{timestamp}.xlsx")
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# 定义列顺序 (表头)
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# 内贸表头
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cols_domestic = [
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"合同编号", "签署公司", "内贸合同号", "收款情况", "签订日期", "销售员",
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"最终用户单位", "最终用户信息联系人", "最终用户信息电话", "最终用户信息邮箱", "最终用户所在地",
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"买方单位", "买方信息联系人", "买方信息电话", "买方信息邮箱",
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"厂家型号", "合同标的", "数量", "单位", "折扣率（％）", "合同额", "合同总额",
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"外购付款方式", "最晚发货期", "已收款", "未收款", "收款日期"
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]
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# 外贸表头 (参考内贸稍作调整)
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cols_foreign = [
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"合同编号", "签署公司", "外贸合同号", "收款情况", "签订日期", "销售员",
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"最终用户单位", "最终用户信息联系人", "最终用户信息电话", "最终用户信息邮箱", "最终用户所在地",
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"买方单位", "买方信息联系人", "买方信息电话", "买方信息邮箱",
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"厂家型号", "合同标的", "数量", "单位", "折扣率（％）", "合同额", "合同总额",
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"外购付款方式", "最晚发货期", "已收款", "未收款", "收款日期"
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]
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# 使用 Pandas ExcelWriter 写入多个 Sheet
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try:
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with pd.ExcelWriter(xlsx_filename, engine='openpyxl') as writer:
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# 1. 写入内贸 Sheet
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if list_domestic:
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df_domestic = pd.DataFrame(list_domestic)
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# 按照指定列顺序排列，如果数据里没有该列会自动填空
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df_domestic = df_domestic.reindex(columns=cols_domestic)
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df_domestic.to_excel(writer, sheet_name='内贸', index=False)
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# 2. 写入外贸 Sheet
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if list_foreign:
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df_foreign = pd.DataFrame(list_foreign)
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df_foreign = df_foreign.reindex(columns=cols_foreign)
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df_foreign.to_excel(writer, sheet_name='外贸', index=False)
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# 3. 写入其他 Sheet
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if list_other:
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df_other = pd.DataFrame(list_other)
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# 其他表也暂用内贸的表头格式
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df_other = df_other.reindex(columns=cols_domestic)
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df_other.to_excel(writer, sheet_name='其他', index=False)
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print(f" ✅ 成功导出多Sheet表格: {os.path.abspath(xlsx_filename)}")
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print(f" - 内贸: {len(list_domestic)} 条")
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print(f" - 外贸: {len(list_foreign)} 条")
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print(f" - 其他: {len(list_other)} 条")
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except ImportError:
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print(" ❌ 错误: 缺少 pandas 或 openpyxl 库。")
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print(" 请在终端运行: pip install pandas openpyxl")
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except Exception as e:
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print(f" ❌ 写入 Excel 失败: {e}")
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except Exception as e:
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print(f"\n❌ 程序发生错误: {e}")
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import traceback
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traceback.print_exc()
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if __name__ == "__main__":
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main() |