What the Data for This Category Looks Like
The data for airports' intelligent due diligence reports comes primarily from four sources: publicly available monthly operation statistics released by Civil Aviation Regional Administrations, official annual operation reports published by airports, real-time traffic monitoring data from air traffic control departments, and third-party air transportation industry databases. Data update follows three schedules: real-time flight takeoff and landing data updates hourly, monthly operation data is released by the 5th of the following month, and annual reports are published by the end of March of the next year. Document structure includes fields such as flight takeoff and landing sorties, passenger throughput, cargo and mail throughput, runway operation duration, and ground support equipment maintenance records. Corresponding units are sorties, person-times, tons, hours, and unit counts respectively.
Constraints Imposed on Workflow Orchestration
Real-time flight takeoff and landing data’s hourly update cycle requires workflow trigger frequency to match the data update schedule, to avoid using outdated historical data. Multi-source data has varying formats: for example, data published by the Civil Aviation Administration uses structured CSV, while airport annual reports use unstructured PDF. This requires configuring multi-format parsing nodes to complete unified field mapping. Annual operation reports can span dozens of pages, so segment parsing and result merging nodes must be configured to prevent single-node processing timeouts. Air traffic control department traffic data interfaces have call frequency limits, so current-limiting nodes must be added to the workflow to avoid triggering interface bans.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Annual airport operation reports can reach dozens of pages, so sufficient parsing time must be reserved |
max_context_length | 8000–12000 characters | Long document parsing requires retaining sufficient context to ensure field extraction accuracy |
http_request_timeout | 30 seconds | Civil aviation public data interfaces have stable responses; overly long timeouts will slow down the overall workflow |
workflow_parallel_limit | 3 | Air traffic control data interfaces have call frequency limits, so control concurrency to avoid triggering bans |
variable_mapping_rule | Precise matching by field name | Aviation airport data has industry-specific field naming to avoid mapping deviations |
retry_count | 2 | Address occasional interface fluctuations; too many retries will increase overall workflow latency |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Symptom: Workflow gets stuck at a middle node with no error logs, and execution progress stalls. Cause:
trigger_cronis not configured, or manually triggered nodes are not bound to correct data source trigger events, resulting in no valid input to start the workflow. - Symptom: Extracted flight takeoff and landing sorties fields contain extra spaces, which cannot be correctly mapped to the due diligence report template. Cause: The prompt did not wrap variable extraction rules with
{}, or did not explicitly require removing leading and trailing spaces in the rules. - Symptom: Calling the Feishu multi-dimensional table interface returns
400 Bad Request, and variables fail to be written. Cause: TheAuthorizationfield of the request header is not correctly configured in the HTTP node, or workflow output variables are not spliced into the JSON format required by the Feishu API.
How to Confirm Proper Configuration
- Manually trigger the workflow, check the output logs of each node, and confirm that the field mapping results of multi-source data meet expectations.
- Simulate high-frequency data update scenarios to test whether the workflow trigger frequency matches the data source update cycle.
- Use an interface current-limiting testing tool to verify that the number of parallel nodes in the workflow does not exceed the call limits of third-party interfaces.
- Check the request headers and parameter splicing of HTTP request nodes, and confirm that output variables can be correctly written to the target system.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.