What this category of data looks like
Aviation airport financial report data primarily comes from Civil Aviation Regional Administration public monthly operation briefings, airport annual audit reports, and public disclosure documents from flight schedule coordination agencies. Monthly operation data is updated within 5 working days of each month. Annual financial reports are disclosed by April 30 of the following year. Document structures typically split into three core modules: core operating indicators, cost composition, and non-aeronautical business revenue. Core fields include passenger throughput, cargo and mail throughput, flight movements, per-passenger non-aeronautical revenue, with corresponding units of 10,000 person-times, tons, sorties, and yuan per person. Some airports also disclose segmented metrics such as flight on-time rate and transfer rate.
What constraints do these characteristics impose on workflow orchestration
High-frequency monthly updates require the workflow to include a scheduled trigger node. Verify that the data release time is later than the preset update node to avoid calling invalid old data. Annual financial report documents have large file sizes, with individual files potentially exceeding 100 pages. Adjust segmented parsing parameters to adapt to long texts. Core fields have inconsistent units. Configure field mapping rules during data extraction to avoid unit confusion for throughput and revenue data. Some public disclosure documents include multimodal content such as flight on-time rate bar charts. Distinguish between plain text and multimodal data processing nodes in the workflow.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Individual aviation airport annual financial report documents have large file sizes, and standard timeout durations are insufficient to complete full parsing |
Chunk size | 800–1200 characters | Core operating indicator paragraphs in aviation airport financial reports typically fall within this length range. Segmentation preserves context integrity while reducing model invocation costs |
Text Content Extraction_Field Mapping | Preset unit validation rules for core fields | This category of data has inconsistent units. Mapping rules prevent unit confusion in extraction results |
Scheduled trigger interval | Once per month | Monthly operation data is updated once per month. Scheduled triggers ensure the workflow calls the latest available data |
Multimodal Model Invocation Switch | Enabled | Some public disclosure documents include multimodal content such as flight on-time rate bar charts. A multimodal-capable model must be invoked to complete parsing |
Tool Invocation Failure Retry Count | 3 times | Aviation airport financial report data sources are dispersed. Some public interfaces may be temporarily unavailable, and retries reduce invocation failure rates |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: The tool invocation node in the workflow returns a
400 Bad Requesterror, prompting that the parameter format does not meet requirements. Cause: Unit validation rules for core fields of aviation airport financial reports are not configured, and the parameters passed to the tool include unexpected unit strings. - Phenomenon: After the text extraction node returns a null value, the workflow directly enters a terminated state without triggering preset user prompt content. Cause: Jump logic for null value branches is not configured in the workflow, and no node is set to wait for user input supplementation.
- Phenomenon: A single financial report document containing multimodal charts is passed to both a plain text model and a multimodal model, resulting in redundant or conflicting results. Cause: Data type classification rules are not configured in the workflow, and no processing nodes are set to distinguish between plain text content and multimodal content.
How to Verify Proper Configuration
- Manually upload an aviation airport monthly operation briefing, check whether the text extraction node correctly maps core fields and validates units.
- Configure a scheduled trigger task, check whether the workflow automatically starts at the preset time and calls the latest public data.
- Upload an annual financial report document containing multimodal charts, check whether the workflow automatically diverts to multimodal model processing.
- Simulate a scenario where text extraction returns a null value, check whether the workflow triggers the specified reply node and waits for user input supplementation.
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.