Smart Factory Use Case
工作流概述
这是一个包含9个节点的复杂工作流,主要用于自动化处理各种任务。
工作流源代码
{
"id": "168",
"name": "Smart Factory Use Case",
"nodes": [
{
"name": "Values higher than 50°C",
"type": "n8n-nodes-base.if",
"position": [
250,
550
],
"parameters": {
"conditions": {
"number": [
{
"value1": "={{$node[\"Data from factory sensors\"].json[\"body\"][\"temperature_celsius\"]}}",
"value2": 50,
"operation": "largerEqual"
}
]
}
},
"typeVersion": 1
},
{
"name": "Data from factory sensors",
"type": "n8n-nodes-base.amqpTrigger",
"position": [
50,
700
],
"parameters": {
"sink": "berlin_factory_01",
"options": {}
},
"credentials": {
"amqp": ""
},
"typeVersion": 1
},
{
"name": "Set sensor data",
"type": "n8n-nodes-base.set",
"position": [
450,
850
],
"parameters": {
"values": {
"number": [
{
"name": "temeprature_fahrenheit",
"value": "={{$node[\"Data enrichment (°C to °F)\"].json[\"temperature_fahrenheit\"]}}"
},
{
"name": "temperature_celsius",
"value": "={{$node[\"Data enrichment (°C to °F)\"].json[\"body\"][\"temperature_celsius\"]}}"
},
{
"name": "machine_uptime",
"value": "={{$node[\"Data from factory sensors\"].json[\"body\"][\"machine_id\"][\"uptime\"]}}"
},
{
"name": "time_stamp",
"value": "={{$node[\"Data from factory sensors\"].json[\"body\"][\"time_stamp\"]}}"
}
],
"string": [
{
"name": "machine_name",
"value": "={{$node[\"Data from factory sensors\"].json[\"body\"][\"machine_id\"][\"name\"]}}"
}
]
},
"options": {}
},
"typeVersion": 1
},
{
"name": "Ingest machine data",
"type": "n8n-nodes-base.crateDb",
"position": [
650,
850
],
"parameters": {
"table": "machine_data",
"columns": "temperature_fahrenheit, temperature_celsius, machine_name, machine_uptime, time_stamp"
},
"credentials": {
"crateDb": ""
},
"typeVersion": 1
},
{
"name": "Ingest incident data",
"type": "n8n-nodes-base.crateDb",
"position": [
850,
450
],
"parameters": {
"table": "incident_data",
"columns": "incident_id, html_url, incident_timestamp"
},
"credentials": {
"crateDb": ""
},
"typeVersion": 1
},
{
"name": "Set incident info",
"type": "n8n-nodes-base.set",
"position": [
650,
450
],
"parameters": {
"values": {
"string": [
{
"name": "incident_id",
"value": "={{$node[\"Create an incident\"].json[\"id\"]}}"
},
{
"name": "html_url",
"value": "={{$node[\"Create an incident\"].json[\"html_url\"]}}"
},
{
"name": "incident_timestamp",
"value": "={{$node[\"Create an incident\"].json[\"created_at\"]}}"
}
]
},
"options": {},
"keepOnlySet": true
},
"typeVersion": 1
},
{
"name": "Create an incident",
"type": "n8n-nodes-base.pagerDuty",
"position": [
450,
450
],
"parameters": {
"title": "=Incident with {{$node[\"Data from factory sensors\"].json[\"body\"][\"machine_id\"][\"name\"]}}",
"additionalFields": {}
},
"credentials": {
"pagerDutyApi": ""
},
"typeVersion": 1
},
{
"name": "Data enrichment (°C to °F)",
"type": "n8n-nodes-base.function",
"position": [
250,
850
],
"parameters": {
"functionCode": "temp_fahrenheit = (items[0].json.body.temperature_celsius * 1.8) + 32;
items[0].json.temperature_fahrenheit = temp_fahrenheit;
return items;"
},
"typeVersion": 1,
"alwaysOutputData": true
},
{
"name": "Do nothing",
"type": "n8n-nodes-base.noOp",
"position": [
450,
640
],
"parameters": {},
"typeVersion": 1
}
],
"active": false,
"settings": {},
"connections": {
"Set sensor data": {
"main": [
[
{
"node": "Ingest machine data",
"type": "main",
"index": 0
}
]
]
},
"Set incident info": {
"main": [
[
{
"node": "Ingest incident data",
"type": "main",
"index": 0
}
]
]
},
"Create an incident": {
"main": [
[
{
"node": "Set incident info",
"type": "main",
"index": 0
}
]
]
},
"Values higher than 50°C": {
"main": [
[
{
"node": "Create an incident",
"type": "main",
"index": 0
}
],
[
{
"node": "Do nothing",
"type": "main",
"index": 0
}
]
]
},
"Data from factory sensors": {
"main": [
[
{
"node": "Data enrichment (°C to °F)",
"type": "main",
"index": 0
},
{
"node": "Values higher than 50°C",
"type": "main",
"index": 0
}
]
]
},
"Data enrichment (°C to °F)": {
"main": [
[
{
"node": "Set sensor data",
"type": "main",
"index": 0
}
]
]
}
}
}
功能特点
- 自动检测新邮件
- AI智能内容分析
- 自定义分类规则
- 批量处理能力
- 详细的处理日志
技术分析
节点类型及作用
- If
- Amqptrigger
- Set
- Cratedb
- Pagerduty
复杂度评估
配置难度:
维护难度:
扩展性:
实施指南
前置条件
- 有效的Gmail账户
- n8n平台访问权限
- Google API凭证
- AI分类服务订阅
配置步骤
- 在n8n中导入工作流JSON文件
- 配置Gmail节点的认证信息
- 设置AI分类器的API密钥
- 自定义分类规则和标签映射
- 测试工作流执行
- 配置定时触发器(可选)
关键参数
| 参数名称 | 默认值 | 说明 |
|---|---|---|
| maxEmails | 50 | 单次处理的最大邮件数量 |
| confidenceThreshold | 0.8 | 分类置信度阈值 |
| autoLabel | true | 是否自动添加标签 |
最佳实践
优化建议
- 定期更新AI分类模型以提高准确性
- 根据邮件量调整处理批次大小
- 设置合理的分类置信度阈值
- 定期清理过期的分类规则
安全注意事项
- 妥善保管API密钥和认证信息
- 限制工作流的访问权限
- 定期审查处理日志
- 启用双因素认证保护Gmail账户
性能优化
- 使用增量处理减少重复工作
- 缓存频繁访问的数据
- 并行处理多个邮件分类任务
- 监控系统资源使用情况
故障排除
常见问题
邮件未被正确分类
检查AI分类器的置信度阈值设置,适当降低阈值或更新训练数据。
Gmail认证失败
确认Google API凭证有效且具有正确的权限范围,重新进行OAuth授权。
调试技巧
- 启用详细日志记录查看每个步骤的执行情况
- 使用测试邮件验证分类逻辑
- 检查网络连接和API服务状态
- 逐步执行工作流定位问题节点
错误处理
工作流包含以下错误处理机制:
- 网络超时自动重试(最多3次)
- API错误记录和告警
- 处理失败邮件的隔离机制
- 异常情况下的回滚操作