What the data for this category looks like
Civil aviation airport ledger data is sourced from internal business systems including operation management systems, flight scheduling systems, ground support systems, and security check statistics systems. Core support ledgers are refreshed hourly. Daily summary operation ledgers are updated each early morning. Documents are stored in structured table format, with one file corresponding to one day’s operation data. Fields include flight number, takeoff and landing time, support node, ground crew team, baggage throughput, and security check passenger volume. Corresponding units are none, hour-minute, none, none, kilogram, and passenger volume, with no nested hierarchy.
What Constraints These Characteristics Impose on Model Access and Configuration
Fields from multi-source structured ledgers have identical-name differences across systems. Clear field mapping rules must be defined during the configuration phase to avoid model recognition errors. Daily summary ledgers have large data volumes. Context-related parameters must be adjusted to support full data reading. Real-time updated support ledgers require models to pull the latest data during calls. A reasonable data source pull cycle must be configured. Fields include civil aviation-specific units. Unit requirements must be clearly stated in the model prompt to avoid unit errors in generated reports.
How to Set Configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxTokens | 8000–12000 token | Adapts to the text length of one day’s full ledger data, preventing key information from being truncated |
temperature | 0.1–0.3 | Ensures the rigor of ledger reports, reducing randomness in generated content |
AIPROXY_API_ENDPOINT | Airport intranet proxy address | Isolates external model calls from internal data sources to ensure data access security |
AIPROXY_API_TOKEN | Exclusive token bound to intranet permissions | Restricts model call requests to authorized internal systems only |
quoteMaxToken | 2000–3000 token | Retains complete context of core ledger fields, ensuring accurate data reference in reports |
maxContext | Calibrated through actual testing | Adapts to the total length of multi-source ledger data, preventing context overflow |
Three Common Errors
- Phenomenon: Model calls return full thought process text, which does not match the expected pure report result. Cause: Intermediate thought output is not disabled in the model configuration, or the prompt does not explicitly require only the final report content to be returned.
- Phenomenon: Model channels cannot be connected via aiproxy, with a connection failure prompt. Cause:
AIPROXY_API_ENDPOINTis not correctly configured as an address accessible via the airport intranet, or intranet access to the proxy service is not authorized. - Phenomenon: Generated ledger reports lack some core field data. Cause: The value of
maxTokensis too small, causing some ledger entries to be truncated and unable to be fully passed to the model.
How to Confirm Configuration Completion
- Initiate a single-day ledger report generation request, and check whether the returned result includes all preset ledger fields and the units meet requirements.
- View model channel call logs, confirm that the verification request for
AIPROXY_API_TOKENpasses, and there are no 403-level errors. - Adjust the value of
maxTokens, compare returned result lengths under different configurations, and confirm that no key data is truncated. - Test access to multi-source ledger data, confirm that the model can correctly map identical-name fields across systems, with no recognition confusion.
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.