Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Rail transit equipment ledger data comes from equipment operation and maintenance management systems, non-destructive testing systems, and parts inventory systems. A single device’s ledger includes fields such as device ID, maintenance date, testing pass status, replaced part model, and cumulative operating duration, with units of unit, count, and hour. Batch summary ledgers are stored in Excel format, while single-device ledgers are structured JSON files. These files are updated immediately after each maintenance or testing operation is completed. Batch ledgers are synchronized across systems every early morning.
Constraints imposed on workflow orchestration by these characteristics
The heterogeneous multi-system sources of rail transit equipment ledgers require configuring cross-data-source pull nodes to adapt to the different data interface formats of the operation and maintenance, testing, and inventory systems. Ledger data with varying update frequencies requires support for two startup modes: event-triggered (triggered immediately after a single operation) and scheduled-triggered (synchronized in batches every early morning). Ledger fields include information such as operating duration with units and part models, so workflow built-in field mapping and unit standardization nodes are needed to avoid inconsistent units during summarization. Batch Excel ledgers have format issues including merged cells and blank rows, so data cleaning nodes must be configured to preprocess raw data.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Data Source Node Type | Multi-source pull combined node | Adapts to the different data interfaces of the operation and maintenance, testing, and inventory systems |
| Trigger Mode | Dual mode: event trigger + scheduled trigger | Covers requirements for single-device immediate updates and daily batch synchronization |
| Field Mapping Rules | Match and associate by device ID, unify operating duration units to hours | Resolves differences in field naming and unit inconsistencies across systems |
| Data Cleaning Threshold | Filter blank rows, split merged cells with a threshold of 800–1200 characters | Adapts to common format abnormalities in batch Excel ledgers |
variables | Preset device ID and statistical cycle variables | Supports generating customized reports for specified devices or cycles |
| Report Generation Template | Preset standard template based on ledger fields | Ensures report structure aligns with rail transit equipment ledger formats |
Three common mistakes
- Phenomenon: Fragments of knowledge base search input and response are mixed in workflow execution output. Cause: The context reference switch was not turned off in workflow nodes, causing knowledge base retrieval content to be carried during tool calls.
- Phenomenon: When using a question classification node in a workflow, the recognition accuracy of rail transit equipment professional terms such as "wheel set testing" is low. Cause: The classification model’s prompt words were not adjusted for the rail transit equipment domain, and general models cannot adapt to professional fields.
- Phenomenon: Workflow timeout errors occur after connecting an external MCP plugin. Cause: The timeout threshold corresponding to
nodeIdwas not configured, and the plugin call duration exceeded the default limit.
How to confirm the configuration is complete
- Manually trigger a single-device ledger pull, and verify that the fields returned by the data source node match the actual ledger field names and content.
- Adjust the statistical cycle variable in
variables, run the workflow, and confirm that the report only includes ledger data within the corresponding cycle. - Import a batch Excel ledger with merged cells, and check whether the data cleaning node has completed format repair and content extraction.
- Disable the knowledge base context reference switch, run the workflow, and verify that no retrieval-related fragments appear in the output.
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.