Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Ledger data for civil aviation airports comes from four core business systems: flight dispatch system, security control system, cargo dispatch system, and ground support system. Each individual ledger record includes fields such as flight number, takeoff and landing time, stand number, passenger throughput, cargo and mail weight, support team, and abnormal event status. Takeoff and landing time uses the YYYY-MM-DD HH:MM format. Cargo and mail weight is measured in kilograms, and passenger throughput is measured in person-times. Data updates follow two rhythms: single abnormal event records are stored in real time after generation, and full batches of ledger data are fully synchronized daily at midnight.
What constraints do these characteristics impose on workflow orchestration
Multi-source data collection requires multiple independent data source nodes to avoid conflicts when pulling data across systems. Field names and units vary across different systems, so unified field mapping rules must be configured in the workflow. The two update rhythms require the workflow to support configuration for pulling data by time interval. The volume of ledger entries is large. Processing too many entries on a single node will cause runtime timeouts, so a reasonable single-processing limit must be set. Abnormal event fields may contain null values, so fault-tolerant rules must be configured to filter invalid entries.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Data Source Node | Create separately for flight, security, and cargo business | Match the independent ledger data sources of multiple airport systems |
variables | Configure as the incremental interval from 2024-01-01 00:00 to the current time, or a scheduled parameter triggered daily at 00:00 | Adapt to the real-time incremental and daily full update rhythms of ledgers |
| Field Mapping Node | Align with the airport ledger standard fields released by the Civil Aviation Administration | Unify field names and units across different business systems |
| Single Processing Entry Count | First 200 entries | Avoid overloading a single workflow node |
nodeId | Assign a unique alphanumeric identifier to each data source node | Facilitate workflow debugging and log locating |
| Exception Filtering Rule | Only retain abnormal event entries with a status of "Completed" | Comply with the compliance requirements for airport ledger reporting |
Three Common Mistakes
- No nodes appear in the interface after importing a workflow template. Cause: The "Retain Node Configuration" option was not checked during import, or the
nodeIdin the template conflicts with node identifiers in the current workspace. - Some nodes in the workflow still display original source content after visibility configuration. Cause: The scope of the visibility switch was not configured in
variables, causing the global configuration to override node-level settings. - The text content extraction node cannot select a specified model, and results are poor after calling the specified model. Cause: Model calling permissions were not enabled in the global workflow settings, the API key for the corresponding model was not bound, or the context length parameter for tool calling was not configured in
variables.
How to Confirm Proper Configuration
- Run the workflow and view node logs to confirm that each data source node successfully pulls ledger data from the corresponding business system.
- Export the intermediate output of the workflow to check whether the field mapping results conform to the ledger standard format released by the Civil Aviation Administration.
- Manually trigger the exception filtering node to confirm that only abnormal events with a status of Completed are retained.
- Adjust the single processing entry count, observe the node running status, and confirm that no overload error occurs.
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.