What this category of data looks like
Aviation airport research report data primarily comes from public monthly statistical reports released by civil aviation authorities, official operational announcements from airport groups, and specialized analysis reports from industry consulting institutions. Routine data is updated monthly. Real-time supplements are made for major operational changes such as new route launches or annual throughput milestones. Most documents are structured PDF reports or long-text analysis content. They include fields like monthly passenger throughput, takeoff and landing sorties, and proportion of international flights. Units are ten thousand person-times, sorties, and percentage respectively. Some data also includes quarterly month-on-month and year-on-year comparison details.
Constraints imposed by these characteristics on workflow orchestration
The multi-source, heterogeneous data characteristics of aviation airport research reports require the workflow to support parsing of both structured CSV files and unstructured PDF reports. The scheduled update requirement for routine monthly data requires configuring the schedule_trigger node to trigger the workflow on a natural monthly cycle. Real-time operational changes require binding an event listening node to connect to the airport group’s announcement push interface. Specific unit requirements for fields require adding unit validation rules during data extraction. This prevents unit confusion during question answering. Long-text analysis reports require setting a reasonable segment length. This preserves the integrity of specialized terms such as "takeoff and landing sorties" and "terminal utilization rate".
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Aviation airport research report PDFs often contain multi-page charts and structured tables, with longer parsing times than general documents |
chunk_size | 800–1200 characters | Research reports in this category have dense specialized terminology. Segments that are too long will break context connections. Segments that are too short will damage logical integrity |
recall_top_k | Top 8 entries | A single aviation airport research report covers effective information across multiple dimensions such as throughput, flights, and costs. Sufficient recall volume is needed to match different queries |
similarity_threshold | 0.72–0.78 | Specialized terms in the aviation field have high distinctiveness. A threshold that is too low will introduce irrelevant industry general reports. A threshold that is too high will miss segmented data |
MCP_ENABLED | true | Connection to public data interfaces of civil aviation authorities and real-time announcement interfaces of airport groups is required. MCP enables external tool calls |
global_var_scope | workflow_level | Global parameters such as airport three-letter codes and statistical cycles need to be reused in the workflow. This avoids repeated configuration |
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 specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Symptom: An error
400 Bad Requestis triggered after configuring the MCP plugin in the workflow, with the prompt "No matching tool definition found". Cause: The API key and interface address of the MCP plugin were not registered in the global configuration in advance. Only call parameters were filled in the workflow node. - Symptom: The code execution node returns no results after running. The log shows
Execution timed out after 30 seconds. Cause: TheCODE_RUN_TIMEOUTparameter value was not adjusted. Batch data cleaning scripts for aviation airport research reports usually require longer execution times. - Symptom: No optional values appear in the knowledge base variable reference dropdown menu. The globally configured airport code parameter cannot be selected. Cause:
global_var_scopewas not set toworkflow_level. Global variables only take effect at the project level and are not exposed to the current workflow.
How to Confirm Correct Configuration
- Trigger a test workflow. Check the output logs of the parsing node. Confirm that all fields from aviation airport research reports are correctly extracted, and no unit confusion occurs.
- Call the MCP plugin test node. Enter simulated civil aviation authority interface request parameters. Confirm that the returned throughput data matches the officially published values.
- Check the variable reference dropdown menu. Confirm that the globally configured airport code and statistical cycle parameters appear in the optional list.
- Adjust the similarity threshold. Compare recall results under different thresholds. Confirm that the number of recalled entries and the matching degree of specialized content meet expectations.
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.