[root@zabbix4 ~]# grep -E '^AlertScriptsPath' /etc/zabbix/zabbix_server.conf

AlertScriptsPath=/usr/lib/zabbix/alertscripts

[root@zabbix4 ~]# cd /usr/lib/zabbix/alertscripts/

[root@zabbix4 /usr/lib/zabbix/alertscripts]# vim zabbix_qwen_wechat.py

#!/usr/bin/env python3

# -*- coding: utf-8 -*-

# 文件名：zabbix_qwen_wechat.py

# 功能：Zabbix告警 → 阿里云通义千问(Qwen)分析 → 企业微信推送

# 部署路径：/usr/lib/zabbix/alertscripts/

# 权限：chmod +x /usr/lib/zabbix/alertscripts/zabbix_qwen_wechat.py

import sys

import json

import requests

import time

import urllib3

# 禁用 SSL 警告（仅当使用 verify=False 时需要，此处保留为可选）

urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)

# ===================== 配置区（请务必修改）=====================

# 阿里云百炼 API 配置（开通服务后获取 API Key）

ALIYUN_API_KEY = "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" # 替换为你的真实 API Key

ALIYUN_MODEL = "qwen-plus" # 可选 qwen-plus / qwen-max / qwen-turbo

# 使用 OpenAI 兼容接口（推荐）

ALIYUN_API_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions"

# 企业微信机器人 Webhook

WECHAT_WEBHOOK = "https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=xxxxxxxxxxxxxxxxxxxxxxxxxxx"

# AI 提示词模板

PROMPT_TEMPLATE = """你是一个资深运维工程师，请分析以下告警：

**发送给**: {who}

**告警主题**: {subject} (告警信息: {message})

**时间**: {time}

请用**不超过300字**说明：

1. 最可能的故障原因

2. 立即处理步骤

3. 预防建议

"""

# ===================== 参数解析 =====================

try:

who = sys.argv[1]

subject = sys.argv[2]

message = sys.argv[3]

event_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())

except IndexError:

print("ERROR: 参数缺失！请按顺序传入：主机名 告警名称 严重等级")

sys.exit(1)

# ===================== Qwen 分析函数（OpenAI兼容模式）=====================

def analyze_with_qwen():

"""调用阿里云通义千问（OpenAI兼容接口）进行告警分析"""

headers = {

"Authorization": f"Bearer {ALIYUN_API_KEY}",

"Content-Type": "application/json"

}

prompt = PROMPT_TEMPLATE.format(

who=who,

subject=subject,

message=message,

time=event_time

)

payload = {

"model": ALIYUN_MODEL,

"messages": [{"role": "user", "content": prompt}],

"temperature": 0.2,

"max_tokens": 300

}

try:

# 发送请求（可临时添加 verify=False 测试 SSL 问题）

response = requests.post(ALIYUN_API_URL, headers=headers, json=payload, timeout=15)

# 打印调试信息（正式使用时请注释或删除）

print(f"DEBUG: HTTP状态码 = {response.status_code}")

print(f"DEBUG: 响应头 = {response.headers}")

print(f"DEBUG: 响应内容预览 = {response.text[:200]}")

# 尝试解析 JSON

result = response.json()

# OpenAI 兼容格式返回结构

if 'choices' in result and len(result['choices']) > 0:

return result['choices'][0]['message']['content']

else:

return f"AI解析失败：响应缺少choices字段，完整响应：{json.dumps(result, ensure_ascii=False)[:200]}"

except requests.exceptions.Timeout:

return "AI服务超时：请求超过15秒未响应"

except requests.exceptions.ConnectionError as e:

return f"网络连接错误：无法连接到阿里云API - {str(e)}"

except requests.exceptions.RequestException as e:

return f"网络请求异常：{str(e)}"

except ValueError as e:

# JSON 解析错误

if 'response' in locals():

return f"API返回非JSON格式 (HTTP {response.status_code}): {response.text[:200]}"

else:

return f"JSON解析异常：{str(e)}"

except Exception as e:

return f"未知错误：{str(e)}"

# ===================== 企业微信推送 =====================

def send_to_wechat(ai_analysis):

"""推送 Markdown 消息到企业微信群"""

markdown_content = f"""

### Zabbix告警通知

**发送給**: {who}

**告警主题**: {subject}

**告警信息**: {message}

**时间**: {event_time}

### AI智能分析

{ai_analysis}

"""

payload = {

"msgtype": "markdown",

"markdown": {

"content": markdown_content

}

}

try:

resp = requests.post(WECHAT_WEBHOOK, json=payload, timeout=10)

resp_json = resp.json()

if resp_json.get('errcode') == 0:

return True

else:

print(f"企业微信返回错误: {resp_json}")

return False

except Exception as e:

print(f"企业微信推送异常: {str(e)}")

return False

# ===================== 主程序 =====================

if __name__ == "__main__":

analysis_result = analyze_with_qwen()

if send_to_wechat(analysis_result):

print(f"SUCCESS: 告警已推送 | AI分析摘要: {analysis_result[:50]}...")

else:

print("ERROR: 企业微信推送失败，请检查 Webhook 配置")

sys.exit(2)

[root@zabbix4 ~]# pip3 install requests

Collecting requests

Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)

|████████████████████████████████| 63 kB 562 kB/s

Collecting charset-normalizer~=2.0.0

Downloading charset_normalizer-2.0.12-py3-none-any.whl (39 kB)

Collecting idna=2.5

Downloading idna-3.10-py3-none-any.whl (70 kB)

|████████████████████████████████| 70 kB 1.8 MB/s

Collecting urllib3=1.21.1

Downloading urllib3-1.26.20-py2.py3-none-any.whl (144 kB)

|████████████████████████████████| 144 kB 3.1 MB/s

Collecting certifi>=2017.4.17

Downloading certifi-2025.4.26-py3-none-any.whl (159 kB)

|████████████████████████████████| 159 kB 3.5 MB/s

Installing collected packages: urllib3, idna, charset-normalizer, certifi, requests

Successfully installed certifi-2025.4.26 charset-normalizer-2.0.12 idna-3.10 requests-2.27.1 urllib3-1.26.20

WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

[root@zabbix4 ~]# chmod +x /usr/lib/zabbix/alertscripts/zabbix_qwen_wechat.py

[root@zabbix4 ~]# mkdir -p /var/log/zabbix

[root@zabbix4 ~]# chown zabbix:zabbix /var/log/zabbix

[root@zabbix4 ~]# chmod 755 /var/log/zabbix

[root@zabbix4 ~]# python3 /usr/lib/zabbix/alertscripts/zabbix_qwen_wechat.py "admin" "测试告警" "CPU负载超过90%"

[root@agent ~]# systemctl stop zabbix-agent

[root@agent ~]# systemctl status zabbix-agent

● zabbix-agent.service - Zabbix Agent

Loaded: loaded (/usr/lib/systemd/system/zabbix-agent.service; disabled; vendor preset: disabled)

Active: inactive (dead)