What this category of data looks like
Rail transit equipment data comes from multiple sources. These include real-time operating condition data collected by on-board sensors, BOM lists from the production link, maintenance ledgers and operation logs. Data updates follow two rhythms. On-board real-time data is pushed at second-level intervals. Maintenance ledgers and parts replacement records are updated with each maintenance cycle. Product BOMs are only adjusted when equipment models are updated. Each single data record has a fixed structure. It includes unique equipment number, cumulative operating duration, fault code, maintenance completion time, replaced part model and specification. Field units include hours, times, millimeters, pieces and other common units.
Constraints imposed on workflow orchestration
Reference only; verify against the target FastGPT release and deployment documentation. Second-level real-time data updates require workflows to use event triggers. Do not use fixed timed triggers, to avoid delayed responses to equipment anomalies. The multi-field, multi-unit record structure requires configuring variables nodes during workflow orchestration. This unifies units and maps fields, preventing unit mismatch errors during calculations or comparisons. The batch update requirement for maintenance ledgers and operation logs requires workflows to support batch data traversal nodes. This adapts to call logic for multiple data source APIs. The correspondence between fault codes and processing processes requires configuring conditional branch nodes in the workflow. This enables automatic diversion of abnormal scenarios.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Preset field mapping rules by equipment type, covering three core fields: cumulative operating duration, fault code, and part model | Rail transit equipment data has many fields and scattered units; preset mappings avoid runtime unit mismatches |
nodeId | Assign unique nodeId to real-time data trigger nodes, conditional branch nodes, and batch processing nodes respectively | Parallel multi-node execution requires nodeId to locate execution status, avoiding process chaos |
| Context window | 800–1200 characters | Rail transit equipment business processing requires full life cycle equipment information; too small a window will lose key maintenance records |
| Batch processing limit | First 20 entries | Single batch of maintenance ledger data has a large volume; limiting entries avoids workflow timeouts |
| API timeout | 60 seconds | On-board data interface responses are affected by network fluctuations; a 60-second timeout covers most normal request durations |
Three common configuration errors
- Phenomenon: Business fields do not update after workflow execution. Logs show the
variablesnode returns empty values. Cause: No field mapping rules for updates are configured in thevariablesnode. The workflow cannot identify business data that needs synchronization. - Phenomenon: Calling the workflow API returns a 404 status code. The interface address is filled with a local test address. Cause: The local test address is not replaced with the formal API address of the deployment environment. The service cannot be accessed.
- Phenomenon: Key equipment information is missing from the business process guidance content output by the workflow. Cause: The context window configuration is too small. Insufficient historical business data is retained, so the AI cannot generate complete guidance.
How to confirm the configuration is correct
- Import a single test record of rail transit equipment data. Trigger workflow execution. Check whether the output fields of the
variablesnode match the preset mapping rules. - Simulate an equipment anomaly event. Trigger the conditional branch node. Confirm whether the workflow automatically diverts to the corresponding processing link based on the fault code.
- Call the workflow API of the deployment environment. Verify that the returned results include business process guidance content and updated field information.
- Adjust the context window configuration. Check whether the workflow can load complete historical business data of the equipment.
The values provided here are common starting points and should be measured 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.