What this category of data looks like
Rail transit equipment document data mainly comes from vehicle BOM systems, process specification documents, and batch quality inspection reports. Data updates are triggered alongside new vehicle model project initiation and mass production milestones. A single update covers all component documents for one batch. Most documents use structured formats, containing fields such as component number, technical parameters, and inspection standards. Units mostly adopt industrial standard units including millimeters, tons, and kilonewtons. The length of individual documents covers a wide range.
What constraints these characteristics impose on workflow orchestration
The batch updates, complex structure, and fixed industrial units of rail transit equipment document data create three types of constraints for workflow orchestration. First, data pulling must be tied to batch production milestones, and real-time triggering is not recommended. Second, variable validation must match the fixed formats and units of fields such as component number and axle load, to avoid parameter mismatches. Third, the length span of individual documents is large, so batch execution requires splitting node execution logic to prevent a single run from exceeding resource limits.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Global variable array assignment | ["component number","axle load (tons)","inspection result"] | Matches the standard field structure of rail transit equipment documents, aligned with data source fields |
| Maximum run count | 1000 | Adapts to the quantity scale of rail transit equipment components per batch, aligns with the platform's batch execution limit |
| Workflow trigger timing | According to batch production plan milestones | Corresponds to the batch nature of data updates, avoids repeatedly pulling unchanged data |
| Variable validation rules | Match industrial standard units | Ensures units of parameters such as axle load and braking pressure meet document requirements |
nodeId association configuration | Bind the quality inspection report node and the AI generation node | Ensures correct data transfer links, avoids disordered node execution order |
| Bulk input character limit | 8000–12000 characters | Adapts to the average length of individual rail transit equipment process documents, prevents node execution timeouts |
Three common errors
- Node connections appear normal but the workflow gets stuck at the current position for a long time with no error prompt. The cause is that the workflow trigger timing is not tied to batch production milestones, and repeatedly pulling redundant unchanged data leads to excessive resource usage.
- Global variable string array assignment does not meet format requirements, with empty fields or misplaced content. The cause is that the array was not configured to match the document field format, and not aligned with the standard field structure of the data source.
- Variable results in AI conversation replies show content overlap, with the later variable result containing content from the earlier one. The cause is that
variablesin the workflow were not independently isolated, and data interference occurs because the variable cache from the previous execution is not cleared.
How to confirm the configuration is correct
- Trigger a single-batch test workflow, check that the node execution order matches the configuration, with no stuck occurrences.
- Review the global variable assignment results, confirm that field names and units match the standard structure of rail transit equipment documents.
- Simulate multi-batch data input, check that variable caches are correctly cleared with no content overlap.
- Verify the
nodeIdassociated node link, confirm that the data transfer path from the quality inspection report node to the AI generation node is correct.
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.