What this type of data looks like
Data comes from alarm reporting interfaces of environmental sensors, video analysis nodes, and fire protection equipment deployed along tunnels. Alerts push to the workflow trigger source within 10 seconds after generation. Data uses structured JSON format. Fields include:
- Device unique identifier
- Alert type code
- Trigger timestamp
- Abnormal parameter value
- Associated tunnel section code
- Original alert text field
Additional fields:
- Alert level (unit: level)
- Abnormal threshold deviation (unit: physical unit of the corresponding sensor, such as pascal, degree Celsius)
- Duration (unit: second)
Constraints for Workflow Orchestration
Second-level pushed alert data requires event trigger mode for workflows. This avoids delays and resource waste from scheduled polling. Structured JSON alert data requires workflow nodes to preset field parsing rules. This adapts to parameter structures of different alert types. Single alerts include multi-dimensional abnormal parameters. This requires adding parameter verification nodes in workflow orchestration to filter invalid alerts. Tunnel section association fields require binding preset section operation and maintenance rule variables in workflows. This enables graded alert judgment. Fluctuations in alert volume during peak hours require configuring concurrency thresholds for nodes. This prevents workflow overload.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
| Trigger Mode | Event trigger (bind tunnel alert API webhook) | Matches the second-level push update rhythm of alert data, avoids delays |
| Variable Binding | variables binds tunnel section codes, operation and maintenance rule sets | Adapts to section association fields in alert data, implements graded judgment |
| Node Timeout Setting | 600 seconds | Covers the full processing flow of a single alert, avoids triggering built-in timeout termination |
| Field Filter Node | Filter by alert level, abnormal parameter threshold | Filters invalid alerts, reduces load on subsequent nodes |
| Concurrency Limit | Process first 20 entries in parallel | Adapts to alert volume fluctuations during tunnel peak hours, avoids workflow overload |
| Node ID Configuration | nodeId marks each functional node | Facilitates troubleshooting of offset errors during workflow runtime |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Misconfigurations
- Symptom: Offset errors appear in workflow runtime logs. Cause: Functional nodes are not marked with
nodeId. This prevents the workflow from accurately locating abnormal nodes during execution, leading to abnormal offset counting. - Symptom: Workflows terminate automatically after exceeding 120 seconds. Cause: The node timeout setting is not configured to 600 seconds or higher. This triggers the built-in workflow timeout limit.
- Symptom: After uploading a TXT document, large models fail to read the content. Cause: Correct request headers and body formats are not configured in the HTTP request node. File content is not correctly mapped to workflow variables.
How to Verify Proper Configuration
- Manually trigger a test alert. Check the workflow runtime logs to confirm the node execution order matches the
nodeIdmarkings. - Submit a simulated alert with a 180-second duration. Confirm the workflow does not terminate early.
- Upload a test TXT document to the workflow node. Check variable binding results to confirm the file content maps correctly to workflow parameters.
- Simulate 10 concurrent alerts. Confirm no node overload errors appear in the workflow.
The values provided here are common starting points, and should be evaluated against the specific samples of the actual deployment.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.