yuebin's blog

使用python3写了个脚本，用来统计月度工作量。本来只是在本地电脑运行的，想想还是把他移植到web端吧。说真的，要我一个个去搜索拉取，我真不敢说自己就能拉出来，而且一点差错都没有，且不说还要制表汇总，琐碎一地。好在，确实可以通过编写脚本来提高效率，不用一个个去拉取，啥也不说了，上代码。
web演示：https://www.thanx.top/re/shell/indexs.php
直连地址，生产环境用：
http://106.15.4.153:8085/re/shell/indexs.php
代码：

import pandas as pd
import argparse

def process_files(file1_path, file2_path, output_filename):
# 读取数据
df_jan = pd.read_excel(file1_path, sheet_name='导出数据')
df_feb = pd.read_excel(file2_path, sheet_name='导出数据')

# 定义项目名称映射规则
project_mapping = {
'无痛胃镜': '无胃',
'无痛肠镜': '无肠',
'EMR/APC': 'EMR',
'止血术': '止血',
'扩张术': '扩张',
'超声内镜': '超声',
'异物摄取': '异物',
'病例数': '总数'
}

# 定义项目顺序（使用映射后的名称）
project_order = [
'胃镜', '无胃', '肠镜', '无肠', '超声', 'EMR', 'ESD', 'ERCP',
'止血', '异物', '扩张', '其他'
]

# 定义统计函数
def count_stats(df):
stats = {
'胃镜': 0,
'无痛胃镜': 0,
'肠镜': 0,
'无痛肠镜': 0,
'超声内镜': 0,
'EMR/APC': 0,
'ESD': 0,
'ERCP': 0,
'止血术': 0,
'异物摄取': 0,
'扩张术': 0,
'其他': 0
}
for _, row in df.iterrows():
category = str(row['检查类别']).lower().strip()
diagnosis = str(row['镜下诊断']).lower().strip()

# 统计检查类别
if '十二指肠镜' in category or 'ercp' in category:
stats['ERCP'] += 1
elif '胃镜' in category and '无痛' not in category:
stats['胃镜'] += 1
elif '无痛胃镜' in category:
stats['无痛胃镜'] += 1
elif '肠镜' in category and '无痛' not in category:
stats['肠镜'] += 1
elif '无痛肠镜' in category:
stats['无痛肠镜'] += 1
elif '超声内镜' in category:
stats['超声内镜'] += 1
else:
stats['其他'] += 1

# 统计镜下诊断
if '扩张' in diagnosis:
stats['扩张术'] += 1
if 'esd' in diagnosis and 'esd术后' not in diagnosis:
stats['ESD'] += 1
if 'emr' in diagnosis or 'apc' in diagnosis:
stats['EMR/APC'] += 1
if '止血' in diagnosis:
stats['止血术'] += 1
if '异物' in diagnosis:
stats['异物摄取'] += 1

# 计算病例数
stats['病例数'] = (
stats['胃镜'] +
stats['无痛胃镜'] +
stats['肠镜'] +
stats['无痛肠镜'] +
stats['超声内镜'] +
stats['ERCP'] +
stats['其他']
)
return stats

# 获取上月和本月的统计数据
stats_jan = count_stats(df_jan)
stats_feb = count_stats(df_feb)

# 计算同比变化
def calculate_change(current, previous):
if previous == 0:
return 0
return round((current - previous) / previous * 100, 2)

# 创建内镜中心工作量统计 DataFrame
center_data = []
for project in project_order:
original_project = next(
(key for key, value in project_mapping.items() if value == project),
project
)
center_data.append({
'项目': project,
'本月数量': stats_feb.get(original_project, 0),
'上月数量': stats_jan.get(original_project, 0),
'同比变化（%）': calculate_change(stats_feb.get(original_project, 0), stats_jan.get(original_project, 0))
})

center_df = pd.DataFrame(center_data)

# 增加汇总行
summary_row = pd.DataFrame({
'项目': ['汇总'],
'本月数量': [
stats_feb['胃镜'] + stats_feb['无痛胃镜'] + stats_feb['肠镜'] +
stats_feb['无痛肠镜'] + stats_feb['超声内镜'] + stats_feb['ERCP'] + stats_feb['其他']
],
'上月数量': [
stats_jan['胃镜'] + stats_jan['无痛胃镜'] + stats_jan['肠镜'] +
stats_jan['无痛肠镜'] + stats_jan['超声内镜'] + stats_jan['ERCP'] + stats_jan['其他']
],
'同比变化（%）': [calculate_change(
stats_feb['胃镜'] + stats_feb['无痛胃镜'] + stats_feb['肠镜'] +
stats_feb['无痛肠镜'] + stats_feb['超声内镜'] + stats_feb['ERCP'] + stats_feb['其他'],
stats_jan['胃镜'] + stats_jan['无痛胃镜'] + stats_jan['肠镜'] +
stats_jan['无痛肠镜'] + stats_jan['超声内镜'] + stats_jan['ERCP'] + stats_jan['其他']
)],
'备注': ['']
})
center_df = pd.concat([center_df, summary_row], ignore_index=True)

# 统计医生工作量
def count_doctor_stats(df):
doctor_stats = {}
for _, row in df.iterrows():
doctor = row['报告医师']
category = str(row['检查类别']).lower().strip()
diagnosis = str(row['镜下诊断']).lower().strip()

if doctor not in doctor_stats:
doctor_stats[doctor] = {
'胃镜': 0,
'无痛胃镜': 0,
'肠镜': 0,
'无痛肠镜': 0,
'超声内镜': 0,
'ERCP': 0,
'EMR/APC': 0,
'ESD': 0,
'止血术': 0,
'扩张术': 0,
'异物摄取': 0,
'其他': 0,
'病例数': 0
}

if '十二指肠镜' in category or 'ercp' in category:
doctor_stats[doctor]['ERCP'] += 1
elif '胃镜' in category and '无痛' not in category:
doctor_stats[doctor]['胃镜'] += 1
elif '无痛胃镜' in category:
doctor_stats[doctor]['无痛胃镜'] += 1
elif '肠镜' in category and '无痛' not in category:
doctor_stats[doctor]['肠镜'] += 1
elif '无痛肠镜' in category:
doctor_stats[doctor]['无痛肠镜'] += 1
elif '超声内镜' in category:
doctor_stats[doctor]['超声内镜'] += 1
else:
doctor_stats[doctor]['其他'] += 1

if '扩张' in diagnosis:
doctor_stats[doctor]['扩张术'] += 1
if 'esd' in diagnosis and 'esd术后' not in diagnosis:
doctor_stats[doctor]['ESD'] += 1
if 'emr' in diagnosis or 'apc' in diagnosis:
doctor_stats[doctor]['EMR/APC'] += 1
if '止血' in diagnosis:
doctor_stats[doctor]['止血术'] += 1
if '异物' in diagnosis:
doctor_stats[doctor]['异物摄取'] += 1

# 计算病例数
doctor_stats[doctor]['病例数'] = (
doctor_stats[doctor]['胃镜'] +
doctor_stats[doctor]['无痛胃镜'] +
doctor_stats[doctor]['肠镜'] +
doctor_stats[doctor]['无痛肠镜'] +
doctor_stats[doctor]['超声内镜'] +
doctor_stats[doctor]['其他'] +
doctor_stats[doctor]['ERCP']
)
return doctor_stats

# 获取上月和本月的医生统计数据
doctor_stats_jan = count_doctor_stats(df_jan)
doctor_stats_feb = count_doctor_stats(df_feb)

# 创建医生工作量统计 DataFrame
doctor_data = []
for doctor, stats in doctor_stats_feb.items():
doctor_data.append({
'医师': doctor,
**{project_mapping.get(k, k): v for k, v in stats.items()}
})

doctor_df = pd.DataFrame(doctor_data)

# 增加汇总行
summary_row = pd.DataFrame({
'医师': ['汇总'],
**{project_mapping.get(k, k): [doctor_df[project_mapping.get(k, k)].sum()] for k in project_order},
'总数': [doctor_df['总数'].sum()]
})
doctor_df = pd.concat([doctor_df, summary_row], ignore_index=True)

# 统计护士工作量
def count_nurse_stats(df):
nurse_stats = {}
for _, row in df.iterrows():
nurse = row['助手']
category = str(row['检查类别']).lower().strip()
diagnosis = str(row['镜下诊断']).lower().strip()

if nurse not in nurse_stats:
nurse_stats[nurse] = {
'胃镜': 0,
'无痛胃镜': 0,
'肠镜': 0,
'无痛肠镜': 0,
'超声内镜': 0,
'ERCP': 0,
'EMR/APC': 0,
'ESD': 0,
'止血术': 0,
'扩张术': 0,
'异物摄取': 0,
'其他': 0,
'病例数': 0
}

if '十二指肠镜' in category or 'ercp' in category:
nurse_stats[nurse]['ERCP'] += 1
elif '胃镜' in category and '无痛' not in category:
nurse_stats[nurse]['胃镜'] += 1
elif '无痛胃镜' in category:
nurse_stats[nurse]['无痛胃镜'] += 1
elif '肠镜' in category and '无痛' not in category:
nurse_stats[nurse]['肠镜'] += 1
elif '无痛肠镜' in category:
nurse_stats[nurse]['无痛肠镜'] += 1
elif '超声内镜' in category:
nurse_stats[nurse]['超声内镜'] += 1
else:
nurse_stats[nurse]['其他'] += 1

if '扩张' in diagnosis:
nurse_stats[nurse]['扩张术'] += 1
if 'esd' in diagnosis and 'esd术后' not in diagnosis:
nurse_stats[nurse]['ESD'] += 1
if 'emr' in diagnosis or 'apc' in diagnosis:
nurse_stats[nurse]['EMR/APC'] += 1
if '止血' in diagnosis:
nurse_stats[nurse]['止血术'] += 1
if '异物' in diagnosis:
nurse_stats[nurse]['异物摄取'] += 1

# 计算病例数
nurse_stats[nurse]['病例数'] = (
nurse_stats[nurse]['胃镜'] +
nurse_stats[nurse]['无痛胃镜'] +
nurse_stats[nurse]['肠镜'] +
nurse_stats[nurse]['无痛肠镜'] +
nurse_stats[nurse]['超声内镜'] +
nurse_stats[nurse]['其他'] +
nurse_stats[nurse]['ERCP']
)
return nurse_stats

# 获取上月和本月的护士统计数据
nurse_stats_jan = count_nurse_stats(df_jan)
nurse_stats_feb = count_nurse_stats(df_feb)

# 创建护士工作量统计 DataFrame
nurse_data = []
for nurse, stats in nurse_stats_feb.items():
nurse_data.append({
'护士': nurse,
**{project_mapping.get(k, k): v for k, v in stats.items()}
})

nurse_df = pd.DataFrame(nurse_data)

# 增加汇总行
summary_row = pd.DataFrame({
'护士': ['汇总'],
**{project_mapping.get(k, k): [nurse_df[project_mapping.get(k, k)].sum()] for k in project_order},
'总数': [nurse_df['总数'].sum()]
})
nurse_df = pd.concat([nurse_df, summary_row], ignore_index=True)

# 保存更新后的Excel文件
with pd.ExcelWriter(output_filename) as writer:
center_df.to_excel(writer, sheet_name='内镜中心工作量统计', index=False)
doctor_df.to_excel(writer, sheet_name='医生工作量统计', index=False)
nurse_df.to_excel(writer, sheet_name='护士工作量统计', index=False)

if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Process Excel files.')
parser.add_argument('file1', help='Path to the first Excel file')
parser.add_argument('file2', help='Path to the second Excel file')
parser.add_argument('output', help='Path to save the output Excel file')

args = parser.parse_args()

process_files(args.file1, args.file2, args.output)

通过悟空的日常安装的istoreos设置成主路由后无论如何设置，都会发现DHCP分配不了IPV4地址，无论怎么设置，最后发现：
root@iStoreOS:~# uci show dhcp.lan
dhcp.lan=dhcp
dhcp.lan.interface='lan'
dhcp.lan.start='100'
dhcp.lan.leasetime='12h'
dhcp.lan.dhcpv4='disabled'
dhcp.lan.force='1'
dhcp.lan.limit='116'

从 uci show 输出可以看出，DHCPv4 被显式禁用了（dhcp.lan.dhcpv4='disabled'），这是导致局域网设备无法获取 IP 地址的直接原因。以下是完整的修复步骤：
#启用 DHCPv4 服务
uci set dhcp.lan.dhcpv4='server' # 启用DHCPv4服务
uci commit dhcp
/etc/init.d/dnsmasq restart

根据您提供的调试信息，您在页面中找到了大量的 div 元素，但没有找到包含聊天消息的特定元素。这表明可能需要更具体的选择器来提取聊天内容。以下是一些步骤来帮助您解决这个问题：

步骤 4: 逐步调试

如果您仍然无法找到消息元素，可以尝试逐步调试：

1. 选择所有 div 元素:

   const allDivs = document.querySelectorAll('div');
   allDivs.forEach(div => {
      console.log(div); // 输出每个 div 的内容
   });

2. 通过特定属性过滤:
   如果某些 div 有特定的属性（如 data-* 属性），您可以使用这些属性来过滤：

   const userMessages = document.querySelectorAll('div[data-role="message"]'); // 根据实际情况调整

   F12打开浏览器调试窗口，输入1中代码，得到了一些html代码，经过分析得到div标签的标识：
   获取到的标签

聊天的主题标题：d8ed659a
我的提问：fbb737a4
ai的思考过程：e1675d8b
AI的解答过程：ds-markdown ds-markdown--block

问Monica，经过几次调整，最终得到如下结果：

// ==UserScript==
// @name         DeepSeek Chat Content Saver
// @match        https://chat.deepseek.com/*
// @version      1.6
// @description  Save chat content as Markdown with auto-save option
// @grant        none
// @run-at       document-idle
// ==/UserScript==

(function() {
    'use strict';

    const CONFIG = {
        BUTTON_ID: 'deepseek-save-btn',
        USER_PREFIX: "你",
        AI_PREFIX: "AI",
        AUTO_SAVE_ENABLED: true, // 配置项：是否启用自动保存
        AUTO_SAVE_INTERVAL: 30000 // 自动保存间隔（毫秒），例如30秒
    };

    function generateMarkdown(messages) {
        let md = `# 聊天记录\n\n`;
        messages.forEach(msg => {
            md += `**${msg.role === 'user' ? CONFIG.USER_PREFIX : CONFIG.AI_PREFIX}**:\n`;
            md += `${msg.text}\n\n---\n\n`;
        });
        md += `> 保存时间: ${new Date().toLocaleString()}\n`;
        return md;
    }

    function downloadFile(content, filename) {
        const blob = new Blob([content], { type: 'text/markdown;charset=utf-8' });
        const url = URL.createObjectURL(blob);
        const a = document.createElement('a');
        a.href = url;
        a.download = filename;
        document.body.appendChild(a); // 需要将链接添加到DOM中
        a.click();
        document.body.removeChild(a); // 下载后移除链接
        setTimeout(() => URL.revokeObjectURL(url), 100);
    }

    function extractMessages() {
        const messages = [];

        // 提取聊天主题标题
        const titleElement = document.querySelector('.d8ed659a');
        let title = titleElement ? titleElement.innerText : '聊天记录';

        // 提取用户提问、AI思考和AI回答，按顺序排列
        const userQuestions = document.querySelectorAll('.fbb737a4');
        const aiThoughts = document.querySelectorAll('.e1675d8b');
        const aiAnswers = document.querySelectorAll('.ds-markdown.ds-markdown--block');

        for (let i = 0; i < userQuestions.length; i++) {
            messages.push({
                role: 'user',
                text: userQuestions[i].innerText
            });
            if (i < aiThoughts.length) {
                messages.push({
                    role: 'ai',
                    text: aiThoughts[i].innerText
                });
            }
            if (i < aiAnswers.length) {
                messages.push({
                    role: 'ai',
                    text: aiAnswers[i].innerText
                });
            }
        }

        console.log('提取的消息:', messages); // 调试输出
        return { title, messages };
    }

    function saveChatRecord() {
        const { title, messages } = extractMessages();
        if (messages.length === 0) throw new Error("未找到对话内容");

        const content = generateMarkdown(messages);
        //const timestamp = new Date().toISOString().replace(/[-:.]/g, "_"); // 格式化时间戳
        //const filename = `${title}_${timestamp}.md`; // 文件名包含时间戳
        const filename = `${title}.md`; // 文件名不包含时间戳

        // 下载文件
        downloadFile(content, filename);
        //alert('保存成功！');
    }

    function startAutoSave() {
        if (CONFIG.AUTO_SAVE_ENABLED) {
            setInterval(() => {
                try {
                    saveChatRecord();
                } catch (error) {
                    console.error('自动保存失败:', error);
                }
            }, CONFIG.AUTO_SAVE_INTERVAL);
        }
    }

    function createSaveButton() {
        const button = document.createElement('button');
        button.id = CONFIG.BUTTON_ID;
        button.innerText = '保存对话';
        button.style.position = 'fixed';
        button.style.bottom = '20px';
        button.style.right = '20px';
        button.style.padding = '15px 30px';
        button.style.fontSize = '16px';
        button.style.backgroundColor = '#4CAF50';
        button.style.color = 'white';
        button.style.border = 'none';
        button.style.borderRadius = '5px';
        button.style.cursor = 'pointer';
        button.style.zIndex = '1000';
        button.style.boxShadow = '0 2px 5px rgba(0, 0, 0, 0.3)';
        document.body.appendChild(button);
        return button;
    }

    function setupButtonHandler(btn) {
        btn.addEventListener('click', saveChatRecord);
    }

    function initialize() {
        const btn = createSaveButton();
        setupButtonHandler(btn);
        startAutoSave(); // 启动自动保存功能
    }

    // 启动
    if (document.readyState === 'complete') initialize();
    else window.addEventListener('load', initialize);
})();

脚本下载：DeepSeek Chat Content Saver-1.6.user.js