我的工作流程2
中级
这是一个Market Research, AI Summarization领域的自动化工作流，包含 12 个节点。主要使用 If, Code, Telegram, HttpRequest, GoogleSheets 等节点。 使用ScrapeGraphAI和Telegram监控LoopNet商业地产机会
前置要求
- •Telegram Bot Token
- •可能需要目标 API 的认证凭证
- •Google Sheets API 凭证
工作流预览
可视化展示节点连接关系，支持缩放和平移
导出工作流
复制以下 JSON 配置到 n8n 导入，即可使用此工作流
{
"id": "VhEwspDqzu7ssFVE",
"meta": {
"instanceId": "f4b0efaa33080e7774e0d9285c40c7abcd2c6f7cf1a8b901fa7106170dd4cda3",
"templateCredsSetupCompleted": true
},
"name": "我的工作流程 2",
"tags": [
{
"id": "DxXGubfBzRKh6L8T",
"name": "Revenue Optimization",
"createdAt": "2025-07-25T16:24:30.370Z",
"updatedAt": "2025-07-25T16:24:30.370Z"
},
{
"id": "IxkcJ2IpYIxivoHV",
"name": "Content Strategy",
"createdAt": "2025-07-25T12:57:37.677Z",
"updatedAt": "2025-07-25T12:57:37.677Z"
},
{
"id": "PAKIJ2Mm9EvRcR3u",
"name": "Trend Monitoring",
"createdAt": "2025-07-25T12:57:37.670Z",
"updatedAt": "2025-07-25T12:57:37.670Z"
},
{
"id": "YtfXmaZk44MYedPO",
"name": "Dynamic Pricing",
"createdAt": "2025-07-25T16:24:30.369Z",
"updatedAt": "2025-07-25T16:24:30.369Z"
},
{
"id": "wJ30mjhtrposO8Qt",
"name": "Simple RAG",
"createdAt": "2025-07-28T12:55:14.424Z",
"updatedAt": "2025-07-28T12:55:14.424Z"
}
],
"nodes": [
{
"id": "b3039a5a-fac6-42bc-8165-df16a3a3e610",
"name": "📋 计划信息",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1152,
144
],
"parameters": {
"color": 2,
"width": 400,
"height": 852,
"content": "# 每日 CRE 扫描器 ⏰"
},
"typeVersion": 1
},
{
"id": "072af1a6-fc7e-435e-bd7e-b68e5eeb6d84",
"name": "每日 CRE 扫描器",
"type": "n8n-nodes-base.scheduleTrigger",
"position": [
-1008,
832
],
"parameters": {
"rule": {
"interval": [
{
"field": "hours",
"hoursInterval": 24
}
]
}
},
"typeVersion": 1.2
},
{
"id": "5239bd82-fc3a-42df-bea8-40e1f0d2c01b",
"name": "📋 数据收集",
"type": "n8n-nodes-base.stickyNote",
"position": [
-752,
144
],
"parameters": {
"color": 3,
"width": 400,
"height": 852,
"content": "# CRE 数据收集器 🕷️"
},
"typeVersion": 1
},
{
"id": "22906cc4-e61a-440d-8a62-61c0633549bb",
"name": "CRE 数据收集器",
"type": "n8n-nodes-base.httpRequest",
"position": [
-608,
816
],
"parameters": {
"url": "https://api.scrapegraphai.com/v1/smartscraper",
"options": {},
"sendBody": true,
"sendHeaders": true,
"authentication": "headerAuth",
"bodyParameters": {
"parameters": [
{
"name": "website_url",
"value": "https://www.loopnet.com/search/commercial-real-estate/"
},
{
"name": "user_prompt",
"value": "Extract commercial real estate listings: property type, size, location, price, lease rate, contact info"
},
{
"name": "output_schema",
"value": "{\"listings\": [{\"id\": \"string\", \"type\": \"string\", \"size_sqft\": \"number\", \"location\": \"string\", \"price\": \"number\", \"lease_rate\": \"number\", \"contact\": \"string\"}]}"
}
]
},
"headerParameters": {
"parameters": [
{
"name": "Content-Type",
"value": "application/json"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "131f2431-f6f1-44b7-87ef-d0cdf7690f3e",
"name": "📋 分析引擎",
"type": "n8n-nodes-base.stickyNote",
"position": [
-352,
144
],
"parameters": {
"color": 4,
"width": 400,
"height": 852,
"content": "# CRE 分析器和仪表板 📊"
},
"typeVersion": 1
},
{
"id": "702d76c3-3229-446b-adaf-2c0d8e751121",
"name": "CRE 分析器和仪表板",
"type": "n8n-nodes-base.code",
"position": [
-192,
864
],
"parameters": {
"jsCode": "// Simplified CRE Analysis & Dashboard\nconst scrapedData = $input.all()[0].json;\nconst listings = scrapedData.listings || [];\n\n// Core analysis\nconst analysis = {\n total_listings: listings.length,\n avg_lease_rate: 0,\n property_types: {},\n top_locations: {},\n opportunities: [],\n alerts: []\n};\n\n// Process listings\nlet totalRate = 0;\nlet rateCount = 0;\n\nlistings.forEach(listing => {\n // Track property types\n const type = listing.type || 'Unknown';\n analysis.property_types[type] = (analysis.property_types[type] || 0) + 1;\n \n // Track locations\n const location = listing.location || 'Unknown';\n analysis.top_locations[location] = (analysis.top_locations[location] || 0) + 1;\n \n // Calculate average rates\n if (listing.lease_rate) {\n totalRate += listing.lease_rate;\n rateCount++;\n }\n \n // Identify opportunities (below market rate)\n if (listing.lease_rate && listing.lease_rate < 20) {\n analysis.opportunities.push({\n id: listing.id,\n type: listing.type,\n location: listing.location,\n rate: listing.lease_rate,\n size: listing.size_sqft,\n opportunity: 'Below Market Rate'\n });\n }\n});\n\nanalysis.avg_lease_rate = rateCount > 0 ? (totalRate / rateCount).toFixed(2) : 0;\n\n// Generate alerts\nif (analysis.opportunities.length > 10) {\n analysis.alerts.push({\n type: 'High Opportunity Count',\n message: `${analysis.opportunities.length} below-market properties found`,\n priority: 'High'\n });\n}\n\nif (analysis.avg_lease_rate > 30) {\n analysis.alerts.push({\n type: 'Rate Alert',\n message: `Average lease rate (${analysis.avg_lease_rate}) above $30/sqft`,\n priority: 'Medium'\n });\n}\n\n// Create dashboard summary\nconst dashboard = {\n generated_at: new Date().toISOString(),\n market_summary: {\n total_properties: analysis.total_listings,\n avg_lease_rate: `${analysis.avg_lease_rate}/sqft`,\n opportunities_found: analysis.opportunities.length,\n active_alerts: analysis.alerts.length\n },\n top_property_types: Object.entries(analysis.property_types)\n .sort(([,a], [,b]) => b - a)\n .slice(0, 5)\n .map(([type, count]) => ({ type, count })),\n top_locations: Object.entries(analysis.top_locations)\n .sort(([,a], [,b]) => b - a)\n .slice(0, 5)\n .map(([location, count]) => ({ location, count })),\n priority_opportunities: analysis.opportunities\n .sort((a, b) => a.rate - b.rate)\n .slice(0, 10),\n alerts: analysis.alerts,\n next_actions: [\n analysis.opportunities.length > 0 ? `Review ${analysis.opportunities.length} opportunities` : 'No immediate opportunities',\n analysis.alerts.length > 0 ? `Address ${analysis.alerts.length} alerts` : 'No alerts requiring action',\n 'Update market analysis tomorrow'\n ]\n};\n\nconsole.log(`CRE Analysis Complete: ${dashboard.market_summary.total_properties} properties, ${dashboard.market_summary.opportunities_found} opportunities`);\n\nreturn [{ json: dashboard }];"
},
"typeVersion": 2
},
{
"id": "422fb0a3-a654-4239-bcf4-3da927626df6",
"name": "📋 决策逻辑",
"type": "n8n-nodes-base.stickyNote",
"position": [
48,
144
],
"parameters": {
"color": 6,
"width": 400,
"height": 852,
"content": "# 机会检查 🎯"
},
"typeVersion": 1
},
{
"id": "9fcce7ef-60c1-422e-9e0c-de5f684d3bdb",
"name": "检查机会",
"type": "n8n-nodes-base.if",
"position": [
192,
848
],
"parameters": {
"options": {},
"conditions": {
"options": {
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "has-opportunities",
"operator": {
"type": "number",
"operation": "gt"
},
"leftValue": "={{ $json.market_summary.opportunities_found }}",
"rightValue": 0
}
]
}
},
"typeVersion": 2
},
{
"id": "864ba85a-3b21-4bde-a6b2-686368436e6d",
"name": "📋 Telegram 设置",
"type": "n8n-nodes-base.stickyNote",
"position": [
448,
-176
],
"parameters": {
"color": 5,
"width": 400,
"height": 852,
"content": "# 发送机会警报 📱"
},
"typeVersion": 1
},
{
"id": "b4b5cccf-a7da-403f-a7fc-13226b929040",
"name": "发送机会警报",
"type": "n8n-nodes-base.telegram",
"position": [
608,
544
],
"webhookId": "10a9dc8f-552a-47e6-9f03-d11b18524867",
"parameters": {
"operation": "send"
},
"typeVersion": 1.2
},
{
"id": "4ce23013-8b82-439a-8e33-89291516078c",
"name": "📋 数据记录",
"type": "n8n-nodes-base.stickyNote",
"position": [
448,
672
],
"parameters": {
"color": 7,
"width": 400,
"height": 916,
"content": "# 记录到 Google Sheets 📈"
},
"typeVersion": 1
},
{
"id": "501df296-9ece-4f93-ac15-d6b753c10813",
"name": "记录到Google表格",
"type": "n8n-nodes-base.googleSheets",
"position": [
576,
1456
],
"parameters": {
"resource": "spreadsheet",
"operation": "appendOrUpdate"
},
"typeVersion": 4.4
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "3d0f65de-749a-42e4-bbfc-b7073454c025",
"connections": {
"Daily CRE Scanner": {
"main": [
[
{
"node": "CRE Data Collector",
"type": "main",
"index": 0
}
]
]
},
"CRE Data Collector": {
"main": [
[
{
"node": "CRE Analyzer & Dashboard",
"type": "main",
"index": 0
}
]
]
},
"Check for Opportunities": {
"main": [
[
{
"node": "Send Opportunity Alert",
"type": "main",
"index": 0
}
]
]
},
"CRE Analyzer & Dashboard": {
"main": [
[
{
"node": "Check for Opportunities",
"type": "main",
"index": 0
},
{
"node": "Log to Google Sheets",
"type": "main",
"index": 0
}
]
]
}
}
}常见问题
如何使用这个工作流？
复制上方的 JSON 配置代码，在您的 n8n 实例中创建新工作流并选择「从 JSON 导入」，粘贴配置后根据需要修改凭证设置即可。
这个工作流适合什么场景？
中级 - 市场调研, AI 摘要总结
需要付费吗？
本工作流完全免费，您可以直接导入使用。但请注意，工作流中使用的第三方服务（如 OpenAI API）可能需要您自行付费。
相关工作流推荐
我的工作流 2
结合 AI 竞品监控和收入优化的自动化动态定价
If
Code
Merge
+8
25 节点vinci-king-01
市场调研
竞品价格监控
带AI组件和警报的价格监控仪表板
If
Code
Slack
+6
15 节点vinci-king-01
市场调研
社交媒体情绪仪表板
基于自定义 AI 的社交媒体情绪分析仪表板（支持 Twitter、Reddit 和 LinkedIn）
If
Code
Slack
+5
15 节点vinci-king-01
市场调研
我的工作流程 2
AI驱动内容差距分析，使用ScrapeGraphAI和战略规划
Code
Google Sheets
Schedule Trigger
+2
18 节点vinci-king-01
市场调研
房地产市场情绪分析
使用ScrapeGraphAI和Telegram分析房地产市场情绪
Code
Telegram
Schedule Trigger
+2
15 节点vinci-king-01
市场调研
房地产
使用ScrapeGraphAI自动发送Zillow房地产列表到Telegram
Code
Telegram
Schedule Trigger
+2
8 节点vinci-king-01
市场调研
