What This Category of Data Looks Like
Multi-source monitoring data fusion draws data from four categories: industrial IoT sensors, handheld patrol terminal report logs, device PLC operation data, and security video streams. Data update rhythms fall into three types:
- Real-time: Sensor data such as vibration and current, updated per second
- Scheduled: Patrol point report data, updated every 10 to 30 minutes
- Event-triggered: Device state snapshots taken when an alarm triggers
The structure of a single data document includes: unique device identifier, ISO format collection timestamp, monitoring type enumeration value, numeric field, corresponding unit, and collection node ID. The monitoring type field is limited to preset enumeration values, and the numeric unit is bound to the monitoring type. For example, vibration monitoring uses mm/s as its unit, and temperature monitoring uses ℃.
What Constraints These Characteristics Impose on the "Workflow Orchestration" Link
Heterogeneous formats, differentiated update rhythms, and bound unit characteristics of multi-source monitoring data create multiple constraints for workflow orchestration.
- Configure a unified format conversion node to align field structures across data from different sources.
- Set orchestration node trigger intervals to match real-time sensor collection frequencies, avoiding data backlog or idle runs.
- Enable support for dynamic temporary processing node mounting to adapt to sudden event-triggered data and enable rapid alarm judgment.
- Add a unit validation node to filter abnormal values and data with mismatched units. Use the collection node ID as the basis for traffic split routing rules.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
variables | {"allowed_units": {"temperature": "℃", "vibration": "mm/s"}, "max_batch": 100} | Preset valid unit set to avoid abnormal data; batch processing 100 entries balances node load and processing efficiency |
| Node trigger interval | 5-60 seconds | Matches collection rhythms of real-time sensors and scheduled patrol data, covering update frequencies of most patrol monitoring scenarios |
| Traffic split rules | Split by the nodeId field | Divides processing links by collection nodes, adapts to multi-source data uploaded concurrently by multiple nodes, and avoids link conflicts |
| Unit validation node | Enable mandatory validation | Matches the binding relationship between monitoring type and unit, filtering invalid data with mismatched values and units |
| Temporary node mounting | Triggered by alarm events | Adapts to rapid alarm judgment needs for sudden alarms, without the need to fix all workflow nodes in advance |
Three Common Mistakes
- Phenomenon: Workflows released via API calls return missing fields, and execution status shows failure. Cause: Required field mapping for multi-source data is not configured in
variables, causing the workflow to fail to recognize incoming device ID and timestamp parameters. - Phenomenon: A single node experiences out-of-memory during 50-concurrency stress testing, and workflow execution times out. Cause: No reasonable batch processing parameter is set, and the number of entries processed in a single batch exceeds the node's memory capacity limit.
- Phenomenon: Defined workflow variables cannot be inserted into the JSON input box, and input content is not parsed correctly. Cause: JSON format variable types are not declared in
variables, causing the system to fail to recognize variable reference syntax.
How to Confirm the Configuration Is Correct
- Upload a single multi-source monitoring data sample, and check if the workflow execution log contains successful markers for format conversion and unit validation.
- Configure a concurrency test script, initiate calls at the actual business concurrency level, and observe if node resource usage is within a reasonable range.
- Trigger a simulated alarm event, and check if the dynamically mounted temporary node starts normally and completes data judgment.
- View the workflow's variable mapping configuration, and confirm that the parameters declared in
variablesfully match the incoming API request parameters.
The values provided here are common starting points and should be evaluated against the reader's own samples.
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.